Sentiment Analysis Tools Overview, Part 2

by Olga Davydova

Information contained in this article has been mostly obtained from sentiment analysis tools’ official websites.

The most common application of sentiment analysis is in consumer products and services reviews. The main task of sentiment analysis is to determine whether the text expresses a positive or a negative sentiment and to assign it a polarity value. The goal of this article is to review the most known sentiment analysis tools. We will cover lexicon-based analysis methods, rule-based analysis methods, and machine learning techniques.

NLTK SentimentAnalyzer

Many sentiment analysis tools rely on lists of words and phrases with positive and negative connotations. Many lists are already available. You can read about the most known lists in the previous article.

Another useful function is demo_vader_instance(text). It returns polarity scores for a text using VADER approach.

Pattern

The sentiment() function returns a (polarity, subjectivity) tuple, an ordered set of values, for the given sentence, based on the adjectives it contains, where polarity is a value between -1.0 and +1.0 and subjectivity between 0.0 and 1.0.

The positive() function returns True if the given sentence’s polarity is above the threshold.

TextBlob

Stanford CoreNLP

RapidMiner

General Architecture for Text Engineering (GATE)

R (Package: Syuzhet)

R (Package: RSentiment)

R (Package: Sentiment Analysis)

Google Cloud Prediction API

Sentiment Classifier using Word Sense Disambiguation (WSD)

IBM Watson Natural Language Understanding

SentiGrade and SentеМotion

Conclusion

Resources

2. http://www.clips.ua.ac.be/pages/pattern-en#sentiment

3. http://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis

4. https://nlp.stanford.edu/sentiment/index.html

5. https://rapidminer.com/solutions/text-mining/

6. https://gate.ac.uk/sentiment/

7. https://github.com/mjockers/syuzhet

8. https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html

9. https://cran.r-project.org/web/packages/RSentiment/index.html

10. https://github.com/sfeuerriegel/SentimentAnalysis

11. https://cloud.google.com/prediction/docs/sentiment_analysis

12. https://github.com/kevincobain2000/sentiment_classifier

13. https://www.ibm.com/watson/developercloud/doc/natural-language-understanding/#sentiment

14. http://www.sentimetrix.com/