Version 30 (modified by Zuzana Nevěřilová, 5 years ago) (diff)


Opinion mining, sentiment analysis

IA161 Advanced NLP Course?, Course Guarantee: Aleš Horák

Prepared by: Zuzana Nevěřilová

State of the Art

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?).


  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
  2. Bing Liu. Sentiment Analysis Tutorial. AAAI-2011, August 8, 2011. Slides available at
  3. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of EMNLP 2002.
  4. 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.
  5. Zhang, L. J., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 8.

Bing Liu's References:

Practical Session

Czech Sentiment Analysis

In this workshop, we try two methods for opinion mining: we use the automatic translation of Liu's Opinion Lexion. Next, we try to compensate drawbacks of the translated lexicon by computing word vectors in a simple way.

Requirements: python 3, jupyter notebook, modules NLTK, scipy, numpy, pandas, sklearn

Files: cestina20.csv, cestina20_annotation.csv, Word_Vectors_and_Sentiment.ipynb, negative-words.txt, negative-words-cs.txt, positive-words.txt, positive-words-cs.txt

  1. Create <YOUR_FILE>, a text file named ia161-UCO-01.txt where UCO is your university ID.
  2. Do tasks marked in the python notebook as TASK X. You don't have to do optional tasks.

Upload <YOUR_FILE>

Do not forget to upload your resulting file to the homework vault (odevzdávárna)?.

Attachments (4)

Download all attachments as: .zip