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Indonesian Social Media Sentiment Analysis with Sarcasm Detection…

- - - - We also employed translated SentiWordNet in
        the sentiment classification
      - Resource of word with sentiment score
  - - - Minimize the vocabulary of terms used in the text message
      - In Indonesian text message of social media such as twitter or facebook, people tend to use slank words than the formal one such as using numeric to replace alphabet, repeating vocal characters, and using common informal words to replace the formal words [2]
      - We converse numeric character into alphabet, such as
        “ga2l” into “gagal”
      - Remove vocal repetition, such as “cemunguuuudh” into “cemungudh”
      - Translate informal words into formal words using dictionary, such as “cemungudh” into “semangat” (high spirit).
    - - Unigram
        
        Word context
        
        One word sentiment may change depends on its word context. For example, the word “mahasiswa” (student) is basically a neutral word but if this word is preceeded by “harga” (price) which makes it into “harga mahasiswa” (low price) then the word becomes a possitive word.
        
        Affix
        
        Word with different affix may have different sentiment. For example “murah” (low) has a possitive sentiment, while “murahan” (twopenny) has a negative sentiment
        
        Unigram is more suitable for Indonesian social media text since the grammars used in Indonesian social media texts are various and informal
        
        The unigram taken from the text is only the term that exists in our translated SentiWordNet. We translated English SentiWordNet into Indonesian using an available statistical machine translation (Google)
        
        In the translation, one Indonesian word with more than one English translation is given the average score of all the English translations
        
        Negation
        
        In order to get this feature, the topic of the text message should be extracted first. In this research, we did the topic extraction manually to determine the topic for each text.
        
        The negation words usually reside before the sentiment word and it can be located two or three words away from the sentiment word. We handled this by multiplying the score of sentiment word closest to the negation word by 1.
        
        “Televisi jaman sekarang tidak begitu bagus karena mahal” (nowadays televisions are notgood because they are expensive), the sentiment word “bagus” (good) is changed into negative because it is preceeded by a negation word (“tidak” or “not”)
        
        Negation words (such as “no” or “not”) tend to change
        sentiment score of a certain word.
      - Negativity
        
        Represents the percentage of the negative sentiment in the topic of the text message.
        
        This feature is intended to catch global information. It gives information about the real sentiment of a certain topic
        
        To get this feature, the topic of the text message should be extracted first. In this research, we did the topic extraction manually
        
        For example, in a topic about Indonesian singer, “Rhoma Irama wants to be Indonesia’s President” has 80% negative sentiment texts in the public. Then, the negativity value of the topic is 80% or 0.8. After determining each topic negativity value, the input text negativity value can be determined by extracting the topic of input text and get the negativity value from the topic.
      - Number of interjection words
        
        Example of interjection words are “aha”, “bah”, “nah”, “wew”, “wow”, “yay”, “uh”, etc
        
        We employ this feature based on our observation that among 100 sarcasm text, there are 20 text with interjection words
        
        If the text is using interjection words, the text has more tendency to be classified into sarcasm text.
      - Question word
        
        This feature is used to classify neutral text. By detecting the question word like “who”, “what”, “when”, “how”, “where”, and “why” it will show that the text has no sentiment value.
      - Feature extraction involves reducing the amount of resources required to describe a large set of data
    - - There are two classification steps. The first classification is to classify each text into three sentiment classes: possitive, negative and neutral
      - The second classification is to classify the sarcasm of the possitive text
      - All the classifications are conducted using several machine learning algorithms such as Naïve Bayes, Maximum Entropy, and Support Vector Machine. These algorithms were chosen because they have shown good accuracy in many text classification task (Pang, Bo., Lee, L., and Vaithyanathan, S.. 2002. Thumb’s up?
        Sentiment Classification using machine learning Techniques)
      - We only classify positive sarcasm text is because almost all of the sarcasm text is looked like positive text, while the real value of the text is negative. Although it may be possible to encounter sarcasm text that has the positive value, the quantity is very low.
      - Naïve Bayes
        
        Often used in automated text classification because it is simple and did not need a lot of data compared to another machine learning algorithm [8]
      - Maximum Entropy
        
        Construct a stochastic model that accurately represents the behaviour of the process. Such a model is a method estimating the conditional probability that, given a context, what will the output from the process.
        
        Maximum Entropy find the most uniform model for each context [9].
      - Support Vector Machine
        
        Maps every feature set into a two dimensional plane and construct a model that based on a linear line that separate the class from the mapped feature set
      - In the first classification, the feature is the unigram, while in the second classification, the features are unigram, negativity and the number of interjection words.
      - Rather than directly classify a text into three classes, at first, this method classify a text into opinion and neutral text. After that, the opinion text will be classified into positive or negative class
- - - - Unigram
      - Sentiment Score
      - Question words