ipython sentiment analysis (twitter)

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.