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@@ -0,0 +1 @@ +/aspect-based-sentiment-analysis-2.0.3.tar.gz diff --git a/python-aspect-based-sentiment-analysis.spec b/python-aspect-based-sentiment-analysis.spec new file mode 100644 index 0000000..d3613fd --- /dev/null +++ b/python-aspect-based-sentiment-analysis.spec @@ -0,0 +1,897 @@ +%global _empty_manifest_terminate_build 0 +Name: python-aspect-based-sentiment-analysis +Version: 2.0.3 +Release: 1 +Summary: Aspect Based Sentiment Analysis: Transformer & Interpretability (TensorFlow) +License: Apache-2.0 +URL: https://github.com/ScalaConsultants/Aspect-Based-Sentiment-Analysis +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/46/32/0dc09df6386a210da548270c434829c296dce90086151f7b68f00a40446b/aspect-based-sentiment-analysis-2.0.3.tar.gz +BuildArch: noarch + +Requires: python3-tensorflow +Requires: python3-transformers +Requires: python3-pytest +Requires: python3-scikit-learn +Requires: python3-ipython +Requires: python3-google-cloud-storage +Requires: python3-testfixtures +Requires: python3-optuna +Requires: python3-spacy + +%description + +### Aspect Based Sentiment Analysis + +The task is to classify the sentiment of potentially long texts for several aspects. +The key idea is to build a modern NLP package which supports explanations of model predictions. +The approximated decision explanations help you to infer how reliable predictions are. +The package is standalone, scalable, and can be freely extended to your needs. +We sum up thoughts in the article: +<br> + +[**Do You Trust in Aspect-Based Sentiment Analysis? Testing and Explaining Model Behaviors**](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/) + +<br> + +There are over 100 repositories on GitHub around sentiment analysis +<sup> +[1](https://github.com/songyouwei/ABSA-PyTorch) +[2](https://github.com/jimmyyfeng/TD-LSTM) +[3](https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis) +[4](https://github.com/peace195/aspect-based-sentiment-analysis) +[5](https://github.com/yardstick17/AspectBasedSentimentAnalysis) +[6](https://github.com/thestrox/Aspect-Based-Sentiment-Analysis) +[7](https://github.com/AlexYangLi/ABSA_Keras) +[8](https://github.com/pedrobalage/SemevalAspectBasedSentimentAnalysis) +[9](https://github.com/ganeshjawahar/mem_absa) +</sup>. +All of them are hard to commercialize and reuse open-source research projects. +We clean up this excellent research. +Please give a star if you like the project. +This is important to keep this project alive. + +<br> + +### Quick Start + +The aim is to classify the sentiments of a text concerning given aspects. +We have made several assumptions to make the service more helpful. +Namely, the text being processed might be a full-length document, +the aspects could contain several words (so may be defined more precisely), +and most importantly, the service should provide an approximate explanation of any decision made, +therefore, a user will be able to immediately infer the reliability of a prediction. + +```python +import aspect_based_sentiment_analysis as absa + +nlp = absa.load() +text = ("We are great fans of Slack, but we wish the subscriptions " + "were more accessible to small startups.") + +slack, price = nlp(text, aspects=['slack', 'price']) +assert price.sentiment == absa.Sentiment.negative +assert slack.sentiment == absa.Sentiment.positive +``` + +Above is an example of how quickly you can start to benefit from our open-source package. +All you need to do is to call the `load` function which sets up the ready-to-use pipeline `nlp`. +You can explicitly pass the model name you wish to use (a list of available models is below), or a path to your model. +In spite of the simplicity of using fine-tune models, we encourage you to build a custom model which reflects your data. +The predictions will be more accurate and stable. + +<br> + +### Pipeline: Keeping the Process in Shape + +The pipeline provides an easy-to-use interface for making predictions. +Even a highly accurate model will be useless if it is unclear how to correctly prepare the inputs and how to interpret the outputs. +To make things clear, we have introduced a pipeline that is closely linked to a model. +It is worth to know how to deal with the whole process, especially if you plan to build a custom model. + + +<p align="middle"> +<img src="examples/images/pipeline.png" width="600" alt=""/> +</p> + + +The diagram above illustrates an overview of the pipeline stages. +As usual, at the very beginning, we pre-process the inputs. +We convert the text and the aspects into a `task` which keeps examples (pairs of a text and an aspect) that we can then further tokenize, encode and pass to the model. +The model makes a prediction, and here is a change. +Instead of directly post-processing the model outputs, we have added a review process wherein +the independent component called the `professor` supervises and explains a model prediction. +The professor might dismiss a model prediction if the model internal states or outputs seem suspicious. +In the article [here], we discuss in detail how the model and the professor work. + +````python +import aspect_based_sentiment_analysis as absa + +name = 'absa/classifier-rest-0.2' +model = absa.BertABSClassifier.from_pretrained(name) +tokenizer = absa.BertTokenizer.from_pretrained(name) +professor = absa.Professor(...) # Explained in detail later on. +text_splitter = absa.sentencizer() # The English CNN model from SpaCy. +nlp = absa.Pipeline(model, tokenizer, professor, text_splitter) + +# Break down the pipeline `call` method. +task = nlp.preprocess(text=..., aspects=...) +tokenized_examples = nlp.tokenize(task.examples) +input_batch = nlp.encode(tokenized_examples) +output_batch = nlp.predict(input_batch) +predictions = nlp.review(tokenized_examples, output_batch) +completed_task = nlp.postprocess(task, predictions) +```` + +Above is an example how to initialize the pipeline directly, +and we revise in code the process being discussed by exposing what calling the pipeline does under the hood. +We have omitted a lot of insignificant details but there's one thing we would like to highlight. +The sentiment of long texts tends to be fuzzy and neutral. +Therefore, you might want to split a text into smaller independent chunks, sometimes called spans. +These could include just a single sentence or several sentences. +It depends on how the `text_splitter` works. +In this case, we are using the SpaCy CNN model, which splits a document into single sentences, +and, as a result each sentence can then be processed independently. +Note that longer spans have richer context information, so a model will have more information to consider. +Please take a look at the pipeline details [here](aspect_based_sentiment_analysis/pipelines.py). + +<br> + +### Supervising Model Predictions + +It's time to explain model reasoning, something which is extremely hard. +The key concept is to frame the problem of explaining a model decision as an independent task wherein +an aux. model, the `pattern recognizer`, predicts patterns (weighted compositions of tokens, presented below) given model inputs, outputs, and internal states. +Due to time constraints, at first we did not want to research and build a trainable pattern recognizer. +Instead, we decided to start with a pattern recognizer that originates from our observations, prior knowledge. +The model, the aspect-based sentiment classifier, is based on the transformer architecture wherein self-attention layers hold the most parameters. +Therefore, one might conclude that understanding self-attention layers is a good proxy to understanding a model as a whole. +Accordingly, there are many articles that show how to explain a model decision +in simple terms, using attention values (internal states of self-attention layers) straightforwardly. +Inspired by these articles, we have also analyzed attention values (processing training examples) to search for any meaningful insights. +This exploratory study has led us to create the `BasicPatternRecognizer` (details are [here](aspect_based_sentiment_analysis/aux_models.py)). + +```python +import aspect_based_sentiment_analysis as absa + +recognizer = absa.aux_models.BasicPatternRecognizer() +nlp = absa.load(pattern_recognizer=recognizer) +completed_task = nlp(text=..., aspects=['slack', 'price']) +slack, price = completed_task.examples + +absa.summary(slack) +absa.display(slack.review) +``` + +<p align="middle"> +<img src="examples/images/slack-patterns.png" width="600" alt=""/> +</p> + +```python +absa.summary(price) +absa.display(price.review) +``` + +<p align="middle"> +<img src="examples/images/price-patterns.png" width="600" alt=""/> +</p> + +The explanations are only useful if they are correct. +To form the basic pattern recognizer, we have made several assumptions (prior beliefs), +therefore we should be careful about interpreting the explanations too literally. +Even if the attention values have thought-provoking properties, for example, +they encode rich linguistic relationships, there is no proven chain of causation. +There are a lot of articles that illustrate various concerns why drawing conclusions about model reasoning +directly from attentions might be misleading. +In the article [here](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/), we validate and analyse explanations in detail. + +<br> + +### Ready-to-Use Models + +In the table below, we present the State of the Art results on the SemEval 2014 evaluation dataset +(dataset details are [here](http://alt.qcri.org/semeval2014/task4/)). +There are two available models for the restaurant and the laptop domains. +The model implementation details [here](aspect_based_sentiment_analysis/models.py). +The hyper-parameters optimization (with the explanation how to train a model) is [here](examples/train_classifier.py). +You can easily reproduce our evaluations, look at the performance tests [here](tests/test_performance.py). + +| Model Name | Acc Rest | Acc Lapt | Release | +| :--- | :---: | :---: | :---: | +|| +| LCF-ATEPC [[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) | 90.18 | 82.29 | Jan 2020 | +| BERT-ADA [[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) | 87.89 | 80.23 | Nov 2019 | +| BAT [[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) | 86.03 | 79.35 | Feb 2020 | +|| +| `classifier-rest-0.2` | 85.17 | +| `classifier-lapt-0.2` | | 79.78 + + +<br> + +### Installation + +You can use the pip: +```bash +pip install aspect-based-sentiment-analysis +``` +Otherwise, clone the code and create the new environment via +[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#): +```bash +git clone git@github.com:ScalaConsultants/Aspect-Based-Sentiment-Analysis.git +conda env create -f=environment.yml +conda activate Aspect-Based-Sentiment-Analysis +``` +The package works with the Python in the version 3.7 (the same as in Colab 2021). + +<br> + +### References + +How to use language models in the Aspect-Based Sentiment Analysis: +- Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL 2019) +[[code]](https://github.com/HSLCY/ABSA-BERT-pair)[[paper]](https://www.aclweb.org/anthology/N19-1035/) +- BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (NAACL 2019) +[[code]](https://github.com/howardhsu/BERT-for-RRC-ABSA)[[paper]](http://arxiv.org/abs/1908.11860) +- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis +[[code]](https://github.com/lixin4ever/BERT-E2E-ABSA)[[paper]](http://arxiv.org/abs/1910.00883) + +Introduction to the BERT interpretability: +- Are Sixteen Heads Really Better than One? +[[code]](https://github.com/pmichel31415/are-16-heads-really-better-than-1)[[paper]](http://arxiv.org/abs/1905.10650) +- A Primer in BERTology: What we know about how BERT works +[[paper]](http://arxiv.org/abs/2002.12327) +- What Does BERT Look At? An Analysis of BERT's Attention +[[code]](https://github.com/clarkkev/attention-analysis)[[paper]](http://arxiv.org/abs/1906.04341) +- Visualizing and Measuring the Geometry of BERT +[[code]](https://github.com/PAIR-code/interpretability)[[paper]](http://arxiv.org/abs/1906.02715) +- Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment +[[paper]](http://arxiv.org/abs/1907.11932) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[paper]](http://arxiv.org/abs/2001.11316) +- Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT +[[paper]](http://arxiv.org/abs/2003.04985) +- exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models +[[code]](https://github.com/bhoov/exbert)[[paper]](http://arxiv.org/abs/1910.05276) +- Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings +[[code]](https://github.com/uhh-lt/bert-sense)[[paper]](http://arxiv.org/abs/1909.10430) +- Attention is not Explanation +[[code]](https://github.com/successar/AttentionExplanation)[[paper]](https://arxiv.org/abs/1902.10186) +- Attention is not not Explanation +[[code]](https://github.com/sarahwie/attention)[[paper]](http://arxiv.org/abs/1908.04626)[[blog post]](https://medium.com/@yuvalpinter/attention-is-not-not-explanation-dbc25b534017) +- Hierarchical interpretations for neural network predictions +[[code]](https://github.com/csinva/hierarchical-dnn-interpretations)[[paper]](https://arxiv.org/abs/1806.05337) +- Analysis Methods in Neural NLP +[[code]](https://github.com/boknilev/nlp-analysis-methods)[[paper]](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00254) +- Visualization for Sequential Neural Networks with Attention +[[code]](https://github.com/HendrikStrobelt/Seq2Seq-Vis) +- NeuroX: Toolkit for finding and analyzing important neurons in neural networks +[[code]](https://github.com/fdalvi/NeuroX)[[paper]](https://arxiv.org/abs/1812.09359) + +The State of the Art results: +- A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction +[[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) +- Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification +[[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) + +Other interesting: +- Multi-Dimensional Explanation of Ratings from Reviews +[[paper]](http://arxiv.org/abs/1909.11386) +- Extracting Syntactic Trees from Transformer Encoder Self-Attentions +[[paper]](http://aclweb.org/anthology/W18-5444) +- Master Thesis: Transfer and Multitask Learning for Aspect-Based Sentiment Analysis Using the Google Transformer Architecture +[[code]](https://github.com/felixSchober/ABSA-Transformer) +- Create interactive textual heat maps for Jupiter notebooks +[[code]](https://github.com/AndreasMadsen/python-textualheatmap) +- A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc +[[code]](https://github.com/huggingface/torchMoji) +- More you can find [here](https://github.com/jiangqn/Aspect-Based-Sentiment-Analysis). + +Developed by [Scalac](https://scalac.io/?utm_source=scalac_github&utm_campaign=scalac1&utm_medium=web) + + + + +%package -n python3-aspect-based-sentiment-analysis +Summary: Aspect Based Sentiment Analysis: Transformer & Interpretability (TensorFlow) +Provides: python-aspect-based-sentiment-analysis +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-aspect-based-sentiment-analysis + +### Aspect Based Sentiment Analysis + +The task is to classify the sentiment of potentially long texts for several aspects. +The key idea is to build a modern NLP package which supports explanations of model predictions. +The approximated decision explanations help you to infer how reliable predictions are. +The package is standalone, scalable, and can be freely extended to your needs. +We sum up thoughts in the article: +<br> + +[**Do You Trust in Aspect-Based Sentiment Analysis? Testing and Explaining Model Behaviors**](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/) + +<br> + +There are over 100 repositories on GitHub around sentiment analysis +<sup> +[1](https://github.com/songyouwei/ABSA-PyTorch) +[2](https://github.com/jimmyyfeng/TD-LSTM) +[3](https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis) +[4](https://github.com/peace195/aspect-based-sentiment-analysis) +[5](https://github.com/yardstick17/AspectBasedSentimentAnalysis) +[6](https://github.com/thestrox/Aspect-Based-Sentiment-Analysis) +[7](https://github.com/AlexYangLi/ABSA_Keras) +[8](https://github.com/pedrobalage/SemevalAspectBasedSentimentAnalysis) +[9](https://github.com/ganeshjawahar/mem_absa) +</sup>. +All of them are hard to commercialize and reuse open-source research projects. +We clean up this excellent research. +Please give a star if you like the project. +This is important to keep this project alive. + +<br> + +### Quick Start + +The aim is to classify the sentiments of a text concerning given aspects. +We have made several assumptions to make the service more helpful. +Namely, the text being processed might be a full-length document, +the aspects could contain several words (so may be defined more precisely), +and most importantly, the service should provide an approximate explanation of any decision made, +therefore, a user will be able to immediately infer the reliability of a prediction. + +```python +import aspect_based_sentiment_analysis as absa + +nlp = absa.load() +text = ("We are great fans of Slack, but we wish the subscriptions " + "were more accessible to small startups.") + +slack, price = nlp(text, aspects=['slack', 'price']) +assert price.sentiment == absa.Sentiment.negative +assert slack.sentiment == absa.Sentiment.positive +``` + +Above is an example of how quickly you can start to benefit from our open-source package. +All you need to do is to call the `load` function which sets up the ready-to-use pipeline `nlp`. +You can explicitly pass the model name you wish to use (a list of available models is below), or a path to your model. +In spite of the simplicity of using fine-tune models, we encourage you to build a custom model which reflects your data. +The predictions will be more accurate and stable. + +<br> + +### Pipeline: Keeping the Process in Shape + +The pipeline provides an easy-to-use interface for making predictions. +Even a highly accurate model will be useless if it is unclear how to correctly prepare the inputs and how to interpret the outputs. +To make things clear, we have introduced a pipeline that is closely linked to a model. +It is worth to know how to deal with the whole process, especially if you plan to build a custom model. + + +<p align="middle"> +<img src="examples/images/pipeline.png" width="600" alt=""/> +</p> + + +The diagram above illustrates an overview of the pipeline stages. +As usual, at the very beginning, we pre-process the inputs. +We convert the text and the aspects into a `task` which keeps examples (pairs of a text and an aspect) that we can then further tokenize, encode and pass to the model. +The model makes a prediction, and here is a change. +Instead of directly post-processing the model outputs, we have added a review process wherein +the independent component called the `professor` supervises and explains a model prediction. +The professor might dismiss a model prediction if the model internal states or outputs seem suspicious. +In the article [here], we discuss in detail how the model and the professor work. + +````python +import aspect_based_sentiment_analysis as absa + +name = 'absa/classifier-rest-0.2' +model = absa.BertABSClassifier.from_pretrained(name) +tokenizer = absa.BertTokenizer.from_pretrained(name) +professor = absa.Professor(...) # Explained in detail later on. +text_splitter = absa.sentencizer() # The English CNN model from SpaCy. +nlp = absa.Pipeline(model, tokenizer, professor, text_splitter) + +# Break down the pipeline `call` method. +task = nlp.preprocess(text=..., aspects=...) +tokenized_examples = nlp.tokenize(task.examples) +input_batch = nlp.encode(tokenized_examples) +output_batch = nlp.predict(input_batch) +predictions = nlp.review(tokenized_examples, output_batch) +completed_task = nlp.postprocess(task, predictions) +```` + +Above is an example how to initialize the pipeline directly, +and we revise in code the process being discussed by exposing what calling the pipeline does under the hood. +We have omitted a lot of insignificant details but there's one thing we would like to highlight. +The sentiment of long texts tends to be fuzzy and neutral. +Therefore, you might want to split a text into smaller independent chunks, sometimes called spans. +These could include just a single sentence or several sentences. +It depends on how the `text_splitter` works. +In this case, we are using the SpaCy CNN model, which splits a document into single sentences, +and, as a result each sentence can then be processed independently. +Note that longer spans have richer context information, so a model will have more information to consider. +Please take a look at the pipeline details [here](aspect_based_sentiment_analysis/pipelines.py). + +<br> + +### Supervising Model Predictions + +It's time to explain model reasoning, something which is extremely hard. +The key concept is to frame the problem of explaining a model decision as an independent task wherein +an aux. model, the `pattern recognizer`, predicts patterns (weighted compositions of tokens, presented below) given model inputs, outputs, and internal states. +Due to time constraints, at first we did not want to research and build a trainable pattern recognizer. +Instead, we decided to start with a pattern recognizer that originates from our observations, prior knowledge. +The model, the aspect-based sentiment classifier, is based on the transformer architecture wherein self-attention layers hold the most parameters. +Therefore, one might conclude that understanding self-attention layers is a good proxy to understanding a model as a whole. +Accordingly, there are many articles that show how to explain a model decision +in simple terms, using attention values (internal states of self-attention layers) straightforwardly. +Inspired by these articles, we have also analyzed attention values (processing training examples) to search for any meaningful insights. +This exploratory study has led us to create the `BasicPatternRecognizer` (details are [here](aspect_based_sentiment_analysis/aux_models.py)). + +```python +import aspect_based_sentiment_analysis as absa + +recognizer = absa.aux_models.BasicPatternRecognizer() +nlp = absa.load(pattern_recognizer=recognizer) +completed_task = nlp(text=..., aspects=['slack', 'price']) +slack, price = completed_task.examples + +absa.summary(slack) +absa.display(slack.review) +``` + +<p align="middle"> +<img src="examples/images/slack-patterns.png" width="600" alt=""/> +</p> + +```python +absa.summary(price) +absa.display(price.review) +``` + +<p align="middle"> +<img src="examples/images/price-patterns.png" width="600" alt=""/> +</p> + +The explanations are only useful if they are correct. +To form the basic pattern recognizer, we have made several assumptions (prior beliefs), +therefore we should be careful about interpreting the explanations too literally. +Even if the attention values have thought-provoking properties, for example, +they encode rich linguistic relationships, there is no proven chain of causation. +There are a lot of articles that illustrate various concerns why drawing conclusions about model reasoning +directly from attentions might be misleading. +In the article [here](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/), we validate and analyse explanations in detail. + +<br> + +### Ready-to-Use Models + +In the table below, we present the State of the Art results on the SemEval 2014 evaluation dataset +(dataset details are [here](http://alt.qcri.org/semeval2014/task4/)). +There are two available models for the restaurant and the laptop domains. +The model implementation details [here](aspect_based_sentiment_analysis/models.py). +The hyper-parameters optimization (with the explanation how to train a model) is [here](examples/train_classifier.py). +You can easily reproduce our evaluations, look at the performance tests [here](tests/test_performance.py). + +| Model Name | Acc Rest | Acc Lapt | Release | +| :--- | :---: | :---: | :---: | +|| +| LCF-ATEPC [[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) | 90.18 | 82.29 | Jan 2020 | +| BERT-ADA [[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) | 87.89 | 80.23 | Nov 2019 | +| BAT [[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) | 86.03 | 79.35 | Feb 2020 | +|| +| `classifier-rest-0.2` | 85.17 | +| `classifier-lapt-0.2` | | 79.78 + + +<br> + +### Installation + +You can use the pip: +```bash +pip install aspect-based-sentiment-analysis +``` +Otherwise, clone the code and create the new environment via +[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#): +```bash +git clone git@github.com:ScalaConsultants/Aspect-Based-Sentiment-Analysis.git +conda env create -f=environment.yml +conda activate Aspect-Based-Sentiment-Analysis +``` +The package works with the Python in the version 3.7 (the same as in Colab 2021). + +<br> + +### References + +How to use language models in the Aspect-Based Sentiment Analysis: +- Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL 2019) +[[code]](https://github.com/HSLCY/ABSA-BERT-pair)[[paper]](https://www.aclweb.org/anthology/N19-1035/) +- BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (NAACL 2019) +[[code]](https://github.com/howardhsu/BERT-for-RRC-ABSA)[[paper]](http://arxiv.org/abs/1908.11860) +- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis +[[code]](https://github.com/lixin4ever/BERT-E2E-ABSA)[[paper]](http://arxiv.org/abs/1910.00883) + +Introduction to the BERT interpretability: +- Are Sixteen Heads Really Better than One? +[[code]](https://github.com/pmichel31415/are-16-heads-really-better-than-1)[[paper]](http://arxiv.org/abs/1905.10650) +- A Primer in BERTology: What we know about how BERT works +[[paper]](http://arxiv.org/abs/2002.12327) +- What Does BERT Look At? An Analysis of BERT's Attention +[[code]](https://github.com/clarkkev/attention-analysis)[[paper]](http://arxiv.org/abs/1906.04341) +- Visualizing and Measuring the Geometry of BERT +[[code]](https://github.com/PAIR-code/interpretability)[[paper]](http://arxiv.org/abs/1906.02715) +- Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment +[[paper]](http://arxiv.org/abs/1907.11932) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[paper]](http://arxiv.org/abs/2001.11316) +- Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT +[[paper]](http://arxiv.org/abs/2003.04985) +- exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models +[[code]](https://github.com/bhoov/exbert)[[paper]](http://arxiv.org/abs/1910.05276) +- Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings +[[code]](https://github.com/uhh-lt/bert-sense)[[paper]](http://arxiv.org/abs/1909.10430) +- Attention is not Explanation +[[code]](https://github.com/successar/AttentionExplanation)[[paper]](https://arxiv.org/abs/1902.10186) +- Attention is not not Explanation +[[code]](https://github.com/sarahwie/attention)[[paper]](http://arxiv.org/abs/1908.04626)[[blog post]](https://medium.com/@yuvalpinter/attention-is-not-not-explanation-dbc25b534017) +- Hierarchical interpretations for neural network predictions +[[code]](https://github.com/csinva/hierarchical-dnn-interpretations)[[paper]](https://arxiv.org/abs/1806.05337) +- Analysis Methods in Neural NLP +[[code]](https://github.com/boknilev/nlp-analysis-methods)[[paper]](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00254) +- Visualization for Sequential Neural Networks with Attention +[[code]](https://github.com/HendrikStrobelt/Seq2Seq-Vis) +- NeuroX: Toolkit for finding and analyzing important neurons in neural networks +[[code]](https://github.com/fdalvi/NeuroX)[[paper]](https://arxiv.org/abs/1812.09359) + +The State of the Art results: +- A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction +[[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) +- Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification +[[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) + +Other interesting: +- Multi-Dimensional Explanation of Ratings from Reviews +[[paper]](http://arxiv.org/abs/1909.11386) +- Extracting Syntactic Trees from Transformer Encoder Self-Attentions +[[paper]](http://aclweb.org/anthology/W18-5444) +- Master Thesis: Transfer and Multitask Learning for Aspect-Based Sentiment Analysis Using the Google Transformer Architecture +[[code]](https://github.com/felixSchober/ABSA-Transformer) +- Create interactive textual heat maps for Jupiter notebooks +[[code]](https://github.com/AndreasMadsen/python-textualheatmap) +- A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc +[[code]](https://github.com/huggingface/torchMoji) +- More you can find [here](https://github.com/jiangqn/Aspect-Based-Sentiment-Analysis). + +Developed by [Scalac](https://scalac.io/?utm_source=scalac_github&utm_campaign=scalac1&utm_medium=web) + + + + +%package help +Summary: Development documents and examples for aspect-based-sentiment-analysis +Provides: python3-aspect-based-sentiment-analysis-doc +%description help + +### Aspect Based Sentiment Analysis + +The task is to classify the sentiment of potentially long texts for several aspects. +The key idea is to build a modern NLP package which supports explanations of model predictions. +The approximated decision explanations help you to infer how reliable predictions are. +The package is standalone, scalable, and can be freely extended to your needs. +We sum up thoughts in the article: +<br> + +[**Do You Trust in Aspect-Based Sentiment Analysis? Testing and Explaining Model Behaviors**](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/) + +<br> + +There are over 100 repositories on GitHub around sentiment analysis +<sup> +[1](https://github.com/songyouwei/ABSA-PyTorch) +[2](https://github.com/jimmyyfeng/TD-LSTM) +[3](https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis) +[4](https://github.com/peace195/aspect-based-sentiment-analysis) +[5](https://github.com/yardstick17/AspectBasedSentimentAnalysis) +[6](https://github.com/thestrox/Aspect-Based-Sentiment-Analysis) +[7](https://github.com/AlexYangLi/ABSA_Keras) +[8](https://github.com/pedrobalage/SemevalAspectBasedSentimentAnalysis) +[9](https://github.com/ganeshjawahar/mem_absa) +</sup>. +All of them are hard to commercialize and reuse open-source research projects. +We clean up this excellent research. +Please give a star if you like the project. +This is important to keep this project alive. + +<br> + +### Quick Start + +The aim is to classify the sentiments of a text concerning given aspects. +We have made several assumptions to make the service more helpful. +Namely, the text being processed might be a full-length document, +the aspects could contain several words (so may be defined more precisely), +and most importantly, the service should provide an approximate explanation of any decision made, +therefore, a user will be able to immediately infer the reliability of a prediction. + +```python +import aspect_based_sentiment_analysis as absa + +nlp = absa.load() +text = ("We are great fans of Slack, but we wish the subscriptions " + "were more accessible to small startups.") + +slack, price = nlp(text, aspects=['slack', 'price']) +assert price.sentiment == absa.Sentiment.negative +assert slack.sentiment == absa.Sentiment.positive +``` + +Above is an example of how quickly you can start to benefit from our open-source package. +All you need to do is to call the `load` function which sets up the ready-to-use pipeline `nlp`. +You can explicitly pass the model name you wish to use (a list of available models is below), or a path to your model. +In spite of the simplicity of using fine-tune models, we encourage you to build a custom model which reflects your data. +The predictions will be more accurate and stable. + +<br> + +### Pipeline: Keeping the Process in Shape + +The pipeline provides an easy-to-use interface for making predictions. +Even a highly accurate model will be useless if it is unclear how to correctly prepare the inputs and how to interpret the outputs. +To make things clear, we have introduced a pipeline that is closely linked to a model. +It is worth to know how to deal with the whole process, especially if you plan to build a custom model. + + +<p align="middle"> +<img src="examples/images/pipeline.png" width="600" alt=""/> +</p> + + +The diagram above illustrates an overview of the pipeline stages. +As usual, at the very beginning, we pre-process the inputs. +We convert the text and the aspects into a `task` which keeps examples (pairs of a text and an aspect) that we can then further tokenize, encode and pass to the model. +The model makes a prediction, and here is a change. +Instead of directly post-processing the model outputs, we have added a review process wherein +the independent component called the `professor` supervises and explains a model prediction. +The professor might dismiss a model prediction if the model internal states or outputs seem suspicious. +In the article [here], we discuss in detail how the model and the professor work. + +````python +import aspect_based_sentiment_analysis as absa + +name = 'absa/classifier-rest-0.2' +model = absa.BertABSClassifier.from_pretrained(name) +tokenizer = absa.BertTokenizer.from_pretrained(name) +professor = absa.Professor(...) # Explained in detail later on. +text_splitter = absa.sentencizer() # The English CNN model from SpaCy. +nlp = absa.Pipeline(model, tokenizer, professor, text_splitter) + +# Break down the pipeline `call` method. +task = nlp.preprocess(text=..., aspects=...) +tokenized_examples = nlp.tokenize(task.examples) +input_batch = nlp.encode(tokenized_examples) +output_batch = nlp.predict(input_batch) +predictions = nlp.review(tokenized_examples, output_batch) +completed_task = nlp.postprocess(task, predictions) +```` + +Above is an example how to initialize the pipeline directly, +and we revise in code the process being discussed by exposing what calling the pipeline does under the hood. +We have omitted a lot of insignificant details but there's one thing we would like to highlight. +The sentiment of long texts tends to be fuzzy and neutral. +Therefore, you might want to split a text into smaller independent chunks, sometimes called spans. +These could include just a single sentence or several sentences. +It depends on how the `text_splitter` works. +In this case, we are using the SpaCy CNN model, which splits a document into single sentences, +and, as a result each sentence can then be processed independently. +Note that longer spans have richer context information, so a model will have more information to consider. +Please take a look at the pipeline details [here](aspect_based_sentiment_analysis/pipelines.py). + +<br> + +### Supervising Model Predictions + +It's time to explain model reasoning, something which is extremely hard. +The key concept is to frame the problem of explaining a model decision as an independent task wherein +an aux. model, the `pattern recognizer`, predicts patterns (weighted compositions of tokens, presented below) given model inputs, outputs, and internal states. +Due to time constraints, at first we did not want to research and build a trainable pattern recognizer. +Instead, we decided to start with a pattern recognizer that originates from our observations, prior knowledge. +The model, the aspect-based sentiment classifier, is based on the transformer architecture wherein self-attention layers hold the most parameters. +Therefore, one might conclude that understanding self-attention layers is a good proxy to understanding a model as a whole. +Accordingly, there are many articles that show how to explain a model decision +in simple terms, using attention values (internal states of self-attention layers) straightforwardly. +Inspired by these articles, we have also analyzed attention values (processing training examples) to search for any meaningful insights. +This exploratory study has led us to create the `BasicPatternRecognizer` (details are [here](aspect_based_sentiment_analysis/aux_models.py)). + +```python +import aspect_based_sentiment_analysis as absa + +recognizer = absa.aux_models.BasicPatternRecognizer() +nlp = absa.load(pattern_recognizer=recognizer) +completed_task = nlp(text=..., aspects=['slack', 'price']) +slack, price = completed_task.examples + +absa.summary(slack) +absa.display(slack.review) +``` + +<p align="middle"> +<img src="examples/images/slack-patterns.png" width="600" alt=""/> +</p> + +```python +absa.summary(price) +absa.display(price.review) +``` + +<p align="middle"> +<img src="examples/images/price-patterns.png" width="600" alt=""/> +</p> + +The explanations are only useful if they are correct. +To form the basic pattern recognizer, we have made several assumptions (prior beliefs), +therefore we should be careful about interpreting the explanations too literally. +Even if the attention values have thought-provoking properties, for example, +they encode rich linguistic relationships, there is no proven chain of causation. +There are a lot of articles that illustrate various concerns why drawing conclusions about model reasoning +directly from attentions might be misleading. +In the article [here](https://rafalrolczynski.com/2021/03/07/aspect-based-sentiment-analysis/), we validate and analyse explanations in detail. + +<br> + +### Ready-to-Use Models + +In the table below, we present the State of the Art results on the SemEval 2014 evaluation dataset +(dataset details are [here](http://alt.qcri.org/semeval2014/task4/)). +There are two available models for the restaurant and the laptop domains. +The model implementation details [here](aspect_based_sentiment_analysis/models.py). +The hyper-parameters optimization (with the explanation how to train a model) is [here](examples/train_classifier.py). +You can easily reproduce our evaluations, look at the performance tests [here](tests/test_performance.py). + +| Model Name | Acc Rest | Acc Lapt | Release | +| :--- | :---: | :---: | :---: | +|| +| LCF-ATEPC [[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) | 90.18 | 82.29 | Jan 2020 | +| BERT-ADA [[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) | 87.89 | 80.23 | Nov 2019 | +| BAT [[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) | 86.03 | 79.35 | Feb 2020 | +|| +| `classifier-rest-0.2` | 85.17 | +| `classifier-lapt-0.2` | | 79.78 + + +<br> + +### Installation + +You can use the pip: +```bash +pip install aspect-based-sentiment-analysis +``` +Otherwise, clone the code and create the new environment via +[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#): +```bash +git clone git@github.com:ScalaConsultants/Aspect-Based-Sentiment-Analysis.git +conda env create -f=environment.yml +conda activate Aspect-Based-Sentiment-Analysis +``` +The package works with the Python in the version 3.7 (the same as in Colab 2021). + +<br> + +### References + +How to use language models in the Aspect-Based Sentiment Analysis: +- Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (NAACL 2019) +[[code]](https://github.com/HSLCY/ABSA-BERT-pair)[[paper]](https://www.aclweb.org/anthology/N19-1035/) +- BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (NAACL 2019) +[[code]](https://github.com/howardhsu/BERT-for-RRC-ABSA)[[paper]](http://arxiv.org/abs/1908.11860) +- Exploiting BERT for End-to-End Aspect-based Sentiment Analysis +[[code]](https://github.com/lixin4ever/BERT-E2E-ABSA)[[paper]](http://arxiv.org/abs/1910.00883) + +Introduction to the BERT interpretability: +- Are Sixteen Heads Really Better than One? +[[code]](https://github.com/pmichel31415/are-16-heads-really-better-than-1)[[paper]](http://arxiv.org/abs/1905.10650) +- A Primer in BERTology: What we know about how BERT works +[[paper]](http://arxiv.org/abs/2002.12327) +- What Does BERT Look At? An Analysis of BERT's Attention +[[code]](https://github.com/clarkkev/attention-analysis)[[paper]](http://arxiv.org/abs/1906.04341) +- Visualizing and Measuring the Geometry of BERT +[[code]](https://github.com/PAIR-code/interpretability)[[paper]](http://arxiv.org/abs/1906.02715) +- Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment +[[paper]](http://arxiv.org/abs/1907.11932) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[paper]](http://arxiv.org/abs/2001.11316) +- Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT +[[paper]](http://arxiv.org/abs/2003.04985) +- exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models +[[code]](https://github.com/bhoov/exbert)[[paper]](http://arxiv.org/abs/1910.05276) +- Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings +[[code]](https://github.com/uhh-lt/bert-sense)[[paper]](http://arxiv.org/abs/1909.10430) +- Attention is not Explanation +[[code]](https://github.com/successar/AttentionExplanation)[[paper]](https://arxiv.org/abs/1902.10186) +- Attention is not not Explanation +[[code]](https://github.com/sarahwie/attention)[[paper]](http://arxiv.org/abs/1908.04626)[[blog post]](https://medium.com/@yuvalpinter/attention-is-not-not-explanation-dbc25b534017) +- Hierarchical interpretations for neural network predictions +[[code]](https://github.com/csinva/hierarchical-dnn-interpretations)[[paper]](https://arxiv.org/abs/1806.05337) +- Analysis Methods in Neural NLP +[[code]](https://github.com/boknilev/nlp-analysis-methods)[[paper]](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00254) +- Visualization for Sequential Neural Networks with Attention +[[code]](https://github.com/HendrikStrobelt/Seq2Seq-Vis) +- NeuroX: Toolkit for finding and analyzing important neurons in neural networks +[[code]](https://github.com/fdalvi/NeuroX)[[paper]](https://arxiv.org/abs/1812.09359) + +The State of the Art results: +- A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction +[[code]](https://github.com/yangheng95/LCF-ATEPC)[[paper]](http://arxiv.org/abs/1912.07976) +- Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification +[[code]](https://github.com/deepopinion/domain-adapted-atsc)[[paper]](http://arxiv.org/abs/1908.11860) +- Adversarial Training for Aspect-Based Sentiment Analysis with BERT +[[code]](https://github.com/akkarimi/Adversarial-Training-for-ABSA)[[paper]](https://arxiv.org/pdf/2001.11316.pdf) + +Other interesting: +- Multi-Dimensional Explanation of Ratings from Reviews +[[paper]](http://arxiv.org/abs/1909.11386) +- Extracting Syntactic Trees from Transformer Encoder Self-Attentions +[[paper]](http://aclweb.org/anthology/W18-5444) +- Master Thesis: Transfer and Multitask Learning for Aspect-Based Sentiment Analysis Using the Google Transformer Architecture +[[code]](https://github.com/felixSchober/ABSA-Transformer) +- Create interactive textual heat maps for Jupiter notebooks +[[code]](https://github.com/AndreasMadsen/python-textualheatmap) +- A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc +[[code]](https://github.com/huggingface/torchMoji) +- More you can find [here](https://github.com/jiangqn/Aspect-Based-Sentiment-Analysis). + +Developed by [Scalac](https://scalac.io/?utm_source=scalac_github&utm_campaign=scalac1&utm_medium=web) + + + + +%prep +%autosetup -n aspect-based-sentiment-analysis-2.0.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-aspect-based-sentiment-analysis -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.3-1 +- Package Spec generated @@ -0,0 +1 @@ +b17e9a2e659707f64552968d0f580c32 aspect-based-sentiment-analysis-2.0.3.tar.gz |