Changes between Version 26 and Version 27 of private/NlpInPracticeCourse/OpinionSentiment

Timestamp:

Sep 15, 2019, 7:39:36 PM (5 years ago)

Author:

Zuzana Nevěřilová

Comment:

--

private/NlpInPracticeCourse/OpinionSentiment

@@ -15 +15 @@
  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]]
  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.
+ 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.
 
 
@@ -23 +24 @@
 === Czech Sentiment Analysis === 
 
-In this workshop, we try several methods for opinion mining. First, we will train a classifier on real Czech data from Seznam.cz (thank you for the data, Seznam.cz guys!).
+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, NLTK module
+Requirements: python 3, jupyter notebook, modules NLTK, scipy, numpy, pandas, sklearn
 
 Files: [raw-attachment:reviews.csv reviews.csv], [raw-attachment:reviews_desamb.csv reviews_desamb.csv], [raw-attachment:reviews_czaccent.csv reviews_czaccent.csv], [raw-attachment:reviews_czaccent_desamb.csv reviews_czaccent_desamb.csv], [raw-attachment:classify.py classify.py]
 
 1. Create `<YOUR_FILE>`, a text file named ia161-UCO-01.txt where UCO is your university ID.
-1. Open `reviews.csv`, observe column meanings (Ack. to Karel Votruba, Karel.Votruba@firma.seznam.cz):
-  1. shop ID
-  1. positive comment
-  1. negative comment
-  1. price/delivery rating
-  1. communication rating
-  1. goods/content rating
-  1. package/return rating
-  1. complaints procedure rating
-1. Run classify.py.
-  {{{
-PYTHONIOENCODING=UTF-8 python classify.py
-}}}
-1. What the most informative features? Do they make sense? Write most informative features to `<YOUR_FILE>` and mark them with + or - depending on whether they make sense. Example (In the example, we can see that the first feature makes sense while the second does not.):
-  * peníze +
-  * s -
-1. Open classify.py, read the code, uncomment print statements, observe the results.
-1. Why the classification is not very good even though the accuracy is over 95 %? Particularly, why the last sentence is not correctly classified? Write your answer at the end of `<YOUR_FILE>`. Feel free to discuss it with the group.
-1. Think of improvements in feature extraction. Currently, the program takes only words as features. Add another feature, i.e. modify the function `document_features()`.
-
-=== Sentiment analysis in English ===
-
-Second, we try the Stanford Core NLP Sentiment Pipeline.
-
-Requirements: Java 8, python, gigabytes of memory
-
-`module add jdk`
-this adds Java 8, you can check it when typing `java -version`
-
-1. Download Stanford Core NLP from http://nlp.stanford.edu/software/corenlp.shtml, unzip it.
-
-2. Try it: e.g. 
-{{{
-java -cp "stanford-corenlp-full-2017-06-09/*" -mx5g \
-    edu.stanford.nlp.sentiment.SentimentPipeline -stdin
-}}}
- or 
-{{{
-java -cp "stanford-corenlp-full-2017-06-09/*" -mx5g \
-    edu.stanford.nlp.sentiment.SentimentPipeline \
-    -file book-reviews.txt
-}}}
-
-The file [raw-attachment:book-reviews.txt book-reviews.txt] is available.
-
-3. In how many classes does Stanford Core NLP classify the sentences? Write it in `<YOUR_FILE>`. Write example of a wrong classification.
+1. Do tasks marked in the python notebook as *TASK X*. You don't have to do optional tasks.
 
 === Upload `<YOUR_FILE>` ===