Changes between Version 38 and Version 39 of private/NlpInPracticeCourse/OpinionSentiment
- Timestamp:
- Sep 14, 2023, 12:10:53 PM (8 months ago)
Legend:
- Unmodified
- Added
- Removed
- Modified
-
private/NlpInPracticeCourse/OpinionSentiment
v38 v39 7 7 == State of the Art == 8 8 9 Sentiment analysis can be seen as a text categorization task (i.e. is the writer's opinion on a discussed topic X or Y?). It consists of detection of the topic (which can be easy in focused reviews) and detection of the sentiment (which is generally difficult). Opinions are sometimes expressed in a very subtle manner (e.g. the sentence ''How could anyone sit through this movie?'' contains no negative word) ![3]. The sentiments are usually simply classified by their polarity (positive, negative) but they can be recognized more in depth (e.g. strongly negative). Recognized opinions are also subject to summarization (e.g.how many people like this new iPhone design?).9 Sentiment analysis can be seen as a text categorization task (i.e., the writer's opinion on a discussed topic X or Y?). It consists of detection of the topic (which can be easy in focused reviews) and detection of the sentiment (which is generally difficult). Opinions are sometimes subtly expressed (e.g., the sentence ''How could anyone sit through this movie?'' contains no negative word). The sentiments are usually classified by their polarity (positive, negative) but can be recognized more in-depth (e.g., strongly negative). Recognized opinions are also subject to summarization (e.g., how many people like this new iPhone design?). 10 10 11 11 === References === 12 12 13 13 1. Bing Liu. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. 2012, 5(1): 1-167. DOI: 10.2200/s00416ed1v01y201204hlt016. Draft version available at [[http://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf]] 14 1. Bing Liu. Sentiment Analysis Tutorial. AAAI-2011, August 8, 2011. Slides available at [[http://www.cs.uic.edu/~liub/FBS/Sentiment-Analysis-tutorial-AAAI-2011.pdf]]15 14 1. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of EMNLP 2002. [[http://www.cs.cornell.edu/home/llee/papers/sentiment.pdf]] 16 1. Liviu P. Dinu and Iulia Iuga. The Naive Bayes classifier in opinion mining: In search of the best feature set. In Alexander Gelbukh, editor, Computational Linguistics and Intelligent Text Processing, volume 7181 of Lecture Notes in Computer Science, pages 556–567. Springer Berlin Heidelberg, 2012.17 15 1. Zhang, L. J., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 8. 18 16 19 20 Bing Liu's References: http://www.cs.uic.edu/~liub/FBS/AAAI-2011-tutorial-references.pdf21 17 22 18 == Practical Session == … … 24 20 === Technical Requirements === 25 21 26 The task will proceed using Python notebook run in web browser in the [https://colab.research.google.com/ Google Colaboratory] environment22 The task will proceed using Python notebook run in a web browser in the [https://colab.research.google.com/ Google Colaboratory] environment 27 23 with the MU G-Suite disk access. 28 24 29 In case of running the codes in a local environment, the requirements are 30 Python 3, jupyter notebook, modules NLTK, scipy, numpy, pandas, and sklearn. 31 25 In the case of running the codes in a local environment, the requirements are 26 Python 3 and Jupyter Notebook. 32 27 33 28 === Sentiment Analysis === 34 29 35 In this workshop, we try two methods for opinion mining. 36 We use the Liu's Opinion Lexion. For Czech SA, we use the automatically translated version. Next, we try to compensate drawbacks of the lexicon by computing word vectors in a simple way. 30 In this workshop, we will experiment with a simple aspect-based sentiment analysis (ABSA). We will use a pretrained model to obtain sentiments about individual sentences. 37 31 38 Access the [https://colab.research.google.com/drive/1j9f28cnFrcRmWKhylP6kXhMwtksnhyCo Python notebook in the Google Colab environment]. Do not forget to save your work if you want to see your changes later, leaving the browser will throw away all changes! 39 40 OR 41 42 (local version) 43 44 45 Files: [raw-attachment:Word_Vectors_and_Sentiment.ipynb Word_Vectors_and_Sentiment.ipynb], [raw-attachment:cestina20.csv cestina20.csv], [raw-attachment:cestina20_annotation.csv cestina20_annotation.csv], 46 [raw-attachment:urban_dictionary.csv urban_dictionary.csv], [raw-attachment:negative-words-en.txt negative-words-en.txt], [raw-attachment:negative-words-cs.txt negative-words-cs.txt], [raw-attachment:positive-words-en.txt positive-words-en.txt], [raw-attachment:positive-words-cs.txt positive-words-cs.txt] 32 Access the [https://colab.research.google.com/drive/1YJcwPaxfHCvg8fTCQ4uJTkEAKYT-C2tk?usp=sharing Python notebook in the Google Colab environment]. Do not forget to save your work if you want to see your changes later; leaving the browser will throw away all changes! 47 33 48 34 1. Create `<YOUR_FILE>`, a text file named ia161-UCO-01.txt where UCO is your university ID. 49 1. Enter the name of the dataset you were working on.50 1. Do tasks marked in the python notebook as TASK X. You don't have to do optional tasks.35 1. Do tasks marked in the python notebook as TASK X. 36 1. Write your results from the notebook (**not the notebook itself**) in `<YOUR_FILE>`. 51 37 52 38 === Upload `<YOUR_FILE>` ===
