A Comparative Study of Emotion Mining on Social Media Data
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Date
2019-04-06
Journal Title
Journal ISSN
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Publisher
Daffodil International University
Abstract
Sentiment analysis or emotion mining is the method of computational and natural
language processing based techniques which are used to extract, identify or
characterize subjective information, such as opinions, comment that expressed in a
given piece of text. The main contributions of this paper involve the sophisticated
categorizations of the trend of research on sentiment analysis and its allied areas as
well as to identify the most and least commonly used feature selection techniques to
find out research gaps for future research. This paperwork gives a detailed analysis of
the recent sentiment analysis schemes and directed towards new avenues for future
research work in this region.
Description
Emotions are a part of parcel of human nature that can be considered as inherited.
Also it has been found that different human being expression of a particular emotion
is uniform. Emotion mining is the method of detecting, analyzing human’s felling
towards different proceedings, services, or any other particular interest. The most
interesting and trending topic for the research field is emotion mining was started
since the 20th century. It is also known as the detection or finding the opinions from
the positive text and negative text. Besides, this era of web 2.0 end user Produced data
over the Internet has prolonged more and more rapidly. Social Media platform and
commercial website. For instance, Facebook, Instagram, Twitter, LinkedIn, Amazon,
IMDB offer a platform to share their experiences, knowledge and views on the recent
trend of politics, economics and other global- critical issue. Emotion mining
accumulates online documents ranging from twitters, Facebook; product reviews
blogs and other social media platform. As we know, Human decision making is
always influenced by others thinking, ideas and opinions. While making any purchase
online consumer usually checks opinions of others about the product. Emotion mining
is the automated mining of attitudes, opinions, and emotions from text, speech, and
database sources through Natural Language Processing (NLP). Emotional mining
implicates classifying opinions in a text into categories like "positive" or ‘negative’ or
"neutral". It’s often mentioned as subjectivity analysis, Sentiment analysis and
appraisal extraction. Sentiments or Opinions contain public generated content about
products, services, policies and politics. Customers have a habit of trust the opinion of
other consumers, particularly those with past experience of a product or service, rather
than company marketing. Social Media are influencing customer preferences by
understanding their approaches and behaviors.
A lot of research work is being done in the field of emotional mining due to its
importance in the marketing level competition and the changing needs of the people.
It requires the usage of a training set for its performance. We present a table
summarizing all the studied work. In this comparative study, we have taken a
systematic literature review process to identify areas well focused by researchers,
least addressed areas are also emphasized giving an opportunity to researchers for
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further work, weakness, strengths, threats and opportunities, whose factors represent,
in a sense, future work to be carried out. We have also tried to identify most and least
commonly used feature selection techniques to find research gaps for future work.
Finally, the emotion mining schemes have been compared in terms of accuracy with
respect to some algorithms. Thus this paperwork provides a detailed analysis of the
recent emotion mining schemes and throws light on new avenues for future research
work in this area.
Keywords
Comparative Literature, Text Data Mining, Social Media