sentiment analysis

21
A presentation by M. Almenea, M. Albidah

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Short lecture on "Sentiment Analysis" at KSU, CCIS, Data mining course spring 14.

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Page 1: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 2: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 3: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

What’s the problem?

Page 4: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

What’s the problem?

Why sentiment analysis?

Page 5: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

What’s the problem?

Why sentiment analysis?

Roadmap

Page 6: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

What’s the problem?

Why sentiment analysis?

Roadmap

Challenges

Page 7: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 8: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

We need to use NLP to study emotions, opinions that are

expressed in text.

Page 9: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

We need to use NLP to study emotions, opinions that are

expressed in text.

I have fallen in love with Python.

Example:

This camera takes great photo, I really like it.

Page 10: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 11: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Companies can use SA tojudge on customer opinions.

As a consequence, No surveys.

Page 12: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Companies can use SA tojudge on customer opinions.

As a consequence, No surveys.

Opinion retrieval: search foropinions.

Page 13: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 14: Sentiment Analysis

System Design:1- Get training data {x1,x2,…xm}.2- String to vector.3- Stemming and stopWords removal.4- Attribute selection.5. Run the system.

A presentation by M. Almenea, M. Albidah

Page 15: Sentiment Analysis

System Design:1- Get training data {x1,x2,…xm}.2- String to vector.3- Stemming and stopWords removal.4- Attribute selection.5. Run the system.

A presentation by M. Almenea, M. Albidah

Page 16: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 17: Sentiment Analysis

[1] Example from Prof. Ronen Feldman

Example[1]:- honda accord and toyota camry are nice sedans.- honda accord and toyota camry are nice sedans, but hardly the best car on the road.

Page 18: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

1- Named Entity Recognition.2- Poor spelling, poor punctuation, poor grammar.3- Language complexity.4- For ArabicNLP: Arabizi is another problem.5- Emotional Symbols - :D ;) etc.. .

Page 19: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

http://nlp.stanford.edu:8080/sentiment/rntnDemo.htmlhttp://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

Page 20: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

Page 21: Sentiment Analysis

A presentation by M. Almenea, M. Albidah

• https://stackoverflow.com/questions/4806176/what-are-the-most-challenging-issues-in-sentiment-analysisopinion-mining

• http://www.lct-master.org/files/MullenSentimentCourseSlides.pdf• http://ijcai13.org/files/tutorial_slides/tf4.pdf