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Ipython sentiment analysis (opinion mining on Tweets)

Add this 3 steps to my Ipython code (CODE WILL BE PROVIDE):

1/ Handle negation :

Sentiment analyses note :

create separate tweet-specific sentiment lexicons for terms in affirmative contexts and in negated contexts :

• automatically determine the average sentiment of a term when occurring in an affirmative context,

• and separately the average sentiment of a term when occurring in a negated context.

2/ Lexicon features :

These features are generated by using three manually constructed sentiment lexicons and two automatically constructed lexicons.

The manually constructed lexicons :

o the NRC Emotion Lexicon

o the MPQA Lexicon

o the Bing Liu Lexicon

The two automatically constructed lexicons :

o the Hashtag Sentiment Lexicon

o the Sentiment140 Lexicon

3/ Text span

With lexicons available, the following features were extracted for a text

span. Here a text span can be a target term, its context, or an entire tweet, depending on the task.

The lexicon features include:

(1) the number of sentiment tokens in a text span; sentiment tokens are word tokens whose sentiment scores are not zero in a lexicon;

(2) the total sentiment score of the text span: SenScore (w);

(3) the maximal score : maxSenScore(w);

(4) the total positive and negative sentiment scores of the text span; (5) the sentiment score of the last token in the text span.

Note that all these features are generated, when applicable, by using each of the sentiment lexicons mentioned above.

Goal : improve accuracy of this model

Skills: Algorithm, Java, Machine Learning, Python, Software Architecture

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