Dynamic Sentiment Analysis Using Multiple Machine Learning Algorithms: A Comparative Knowledge Methodology

  • Manmeet Kaur
  • Krishna Kant Agrawal
  • Deepak Arora
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 38)

Abstract

Human can easily understand or interpret the meaning of language. However, a machine has no natural language to deduce the hidden emotions. Without knowing the context of the word, it cannot simply infer whether a piece of text conveys joy, anger or frustration. Here, sentiment analysis came into picture. Sentiment analysis is the analysis of feelings, attitude and opinions of human emotions extracted from text. It uses natural language processing (NLP) for classifying the text into positive, negative or neutral category. Many businesses nowadays take feedback of the product from the customers to improve the quality or service of the product. Earlier feedbacks were taken by the call center executives but today a vast amount of data is available on the Internet. People share their views regarding products, services, people, etc. Sentiment analysis makes the task easier by extracting the relevant words from the sentences and classifying it in different categories. In this paper, we have described the essential steps used in the process of the sentiment analysis and few fields that work under its umbrella. A comparative analysis of machine learning algorithm like Naive Bayes, SVM, maximum entropy is done along with the few algorithms like artificial neural network and K-nearest neighbor, which can be used in sentiment analysis.

Keywords

Machine learning Support vector machine Naive Bayes Maximum entropy Sentiment classification Building resource Transfer learning Emotion detection Chi square Information gain 

References

  1. 1.
    Thakkar H, Patel D (2015) Approaches for sentiment analysis on twitter: a state-of-art study, 1–8Google Scholar
  2. 2.
    Pak A, Paroubek P, Paris-sud D, Limsi-cnrs L, Cedex FO (2010) Twitter based system: using twitter for disambiguating sentiment ambiguous adjectives. Comput Linguist, 436–439Google Scholar
  3. 3.
    Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5:1093–1113CrossRefGoogle Scholar
  4. 4.
    Jiawei H, Kamber M (2001) Data mining: concepts and techniquesGoogle Scholar
  5. 5.
    Shahana PH, Omman B (2015) Evaluation of features on sentimental analysis. Procedia Comput Sci 46:1585–1592CrossRefGoogle Scholar
  6. 6.
    Tripathy A, Agrawal A, Rath SK (2015) Classification of sentimental reviews using machine learning techniques. Procedia Comput Sci 821–829CrossRefGoogle Scholar
  7. 7.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up: sentiment classification using machine learning techniques. Proc Conf Empir Methods Nat Lang Process, 79–86Google Scholar
  8. 8.
    Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, pp 61–67Google Scholar
  9. 9.
    Mehra N, Khandelwal S, Patel P (2002) Sentiment identification using maximum entropy analysis of movie reviewsGoogle Scholar
  10. 10.
    Raychaudhuri S, Chang JT, Sutphin PD, Altman RB (2002) Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature. Genome Res 12:203–214CrossRefGoogle Scholar
  11. 11.
    Moraes R, Valiati JF, Gavião Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANNGoogle Scholar
  12. 12.
    Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis Support Syst 50:511–521CrossRefGoogle Scholar
  13. 13.
    Plutchik R (1980) A general psychoevolutionary theory of emotion. Emot Theor Res Exp 1:3–33Google Scholar
  14. 14.
    Robaldo L, Di Caro L (2013) OpinionMining-ML. Comput Stand Interfaces 35:454–469CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Manmeet Kaur
    • 1
  • Krishna Kant Agrawal
    • 1
  • Deepak Arora
    • 1
  1. 1.Department of Computer Science and EngineeringAmity UniversityNoidaIndia

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