Sentiment Analysis Challenges

By Anonymous -

 

Sentiment analysis is an very active research area in natural language processing(NLP). It’s objective at identifying, extracting and organizing sentiments from user generated texts in product reviews, social networks also on blogs. Sentiment analysis is the process of studying people's emotions and opinions. Sentiment analysis is one of the difficult tasks in natural language processing since even humans struggle to analyze sentiments very accurately or precisely.


Below here, I want to show some issue that you face working on the sentiment analysis challenges ; 

  1. Sarcasm Detection.
  2. Negation Detection.
  3. Word Ambiguity.
  4. Multipolarity.

  1. Sarcasm Detection: Sarcasm turn out mostly in user generated content such as what’s app chatting, Instagram comments, tweets, Facebook comments etc. In sarcastic text, Using positive words, People express or reveal  their negative sentiments. This truth accept sarcasm, To cheat sentiment analysis models till they are not especially designed to take its possibility into account.  Let’s see the example: 
    1. "It's -25 deg. cel. outside and I am so hot." (Sarcastic)
  2. Negation Detection: The absence or opposite of something actual or positive. In linguistics, To reverse the polarity of words, phrases, and even sentences, Negation is a way. To identify whether negation is occurring there are various rules but, It is  also very important to measure the range of the words that are affected by negation words. If a positive or negative word falls inside the scope of negation then the original meaning of the words changes for this case opposite polarity will be returned. For example "The show was not interesting,”. Negation can be morphological where it is either denoted by a suffix (“-less”) or a prefix (“dis-”, “non-”).
  3. Word Ambiguity: Word ambiguity is another pitfall you will face, While working on a sentiment analysis problems. the polarity for some words is strongly dependent on the sentence context so the problem of word ambiguity is the impossible action to define polarity in advance. Examples are:
    1. The story is unpredictable.
    2. The steering wheel is unpredictable.
  4. Multipolarity: Whatever unit of text we would like to analyze, Will exhibit multipolarity. Can be confusing if having only the total result of the analysis, Very much like how an average can sometimes hide valuable information about all the numbers that went into it. The total sentiment polarity will be lost of key information. That's why its  necessary to extract all the aspects in the sentence with assigned or given sentiment labels and only calculate the total polarity if needed. Example which consists of multiple polarities:
    1. “The audio quality of my new laptop is so cool, But the display colors are not too good.”

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