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A hybrid approach for Sarcasm Detection of Social Media Data (Information,…

- - - - Lexicon based approach (N-grams=Bigrams)
        
        Consists of bag of words and trained dataset to determine sarcasm in a tweet tex
        
        Patterns can be created by concatenating adjacent tokens into ngrams where n is the max value on which the all possible combinations can be made for a single pattern
        
        N-gram process is being checked for all bigram values and marked as sarcastic or non-sarcastic
      - Hyperbole approach
        
        It is based on a logic where we consider a unigram set of trained dataset with pragmatic markers solution is able to determine the sarcasm in a text. Unigram dataset contains set of intensifiers (’wow’,’Awesome’,’nottttt’) which expresses the emotions as exaggerations (as intensifiers’) of a user.
        
        And the pragmatic markers are a way expressing words by representing them with various font styles (as [WRITING IN SQUARE BRACKETS] or “WRITING IN CAPITAL LETTERS” or use of “Exclamatory!!!!!!!!! FULLSTOPS……….. /delimiters” etc...).
        
        Hence a proper solution is built using the above logic and we were able to determine sarcasm in texts more often.
        
        Kreuz and Roberts 1995 theoretical work has stressed the importance of hyperbole for sarcasm, we have implemented indicator logic for whether the tweet contains a word in a list of intensifiers (so, too, very, really), stretched words (rightttt), capitalized words ([HELLO]), punctuations and so on. Hence giving us a more stable classifier hence covering most of the uncovered area for text classification
      - Hierarchical approach
        
        Determines text as sarcastic through a sequential logic which takes into considerations.
        
        Hybrid Based
        
        It’s a sequential logic of all the above approaches (refer to figure 6). The tweets are marked as sarcastic if it’s found true in anyone of the rule except for in hashtag rule (Since #tag rule tags all tweets as sarcastic) the implementation is simplified using hierarchical directory format and shown in the
        
        Hierarchical Plotting table
        
        : If the tweets are marked as sarcastic for a sequential logic of contrast with lexicon, hyperbole and emoticon approaches. Then tweet is automatically considered as sarcastic and tagged under hierarchical rule as shown in fig 8.
      - Hybrid approach
        
        Combinational logic of all the given approaches as shown in fig 1,which gives a high precise rate of sarcasm in a text and it was shown that it gave wonderful results compared to all the other approaches.
        
        Hybrid Based
        
        : If the tweets are marked as sarcastic for a sequential logic of contrast with lexicon, hyperbole and emoticon approaches. Then tweet is automatically considered as sarcastic and tagged under hierarchical rule as shown in fig 8.
        
        Hybrid Plotting table
        
        Tweets are marked as sarcastic for a combinational logic for either contrast with hyperbole, or contrast with lexicon, or lexicon with hyperbole, or lexicon with emoticon or contrast with emoticon and emoticon with hyperbole then tweet is automatically considered as sarcastic and tagged under hybrid rule
      - Contrast approach
        
        The Contrast approach uses a logic i.e. a tweet having a positive sentiment and negative situations is considered to sarcastic and are marked as sarcastic
        
        We have applied the POS tagger and respective valence value from Warner et al. (2013) Dataset which includes features based on the absolute count and ratio of each tweet, along with the “lexical density” of the tweet, which models the ratio of nouns, verbs, adjectives and adverbs to all words a baseline capacitor (General Dictionary) is developed and implemented for productive analysis and classified data as shown in fig 3
      - Emoticon based approach
        
        Since the twitter platform allows the user to use emoji to express their sentiments, hence a rule can be built based on the given feature.
        
        Emoticon based approach is used to determine text based on a logic having a positive sentiment tweet with negative emoticons and negative sentiment tweets with positive emoticon can be assumed to be sarcastic In many instances
      - Hash tag based approach
        
        From tweetdict import tweets:
        For each tweet in tweets:
        For data in tweets:
        If data==#Sarcasm-Data:
        Append ‘Sarcastic’ to [Output-list]
        Else:
        Append ‘Non-Sarcastic’ to [Output-list]
        For tweet in tweets:
        Forlistdata in Output-list: Newlst=Join (tweet with list-data)
        Output: [Newlist] of tweets
        
        : :