Skip to main content
Top

2021 | OriginalPaper | Chapter

Comparative Analysis of Sentiment Analysis Between All Bigrams and Selective Adverb/Adjective Bigrams

Authors : Mounicasri Valavala, Hemalatha Indukuri

Published in: Microelectronics, Electromagnetics and Telecommunications

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Increase in the amount of unstructured data across different platforms serves as a valuable resource for predicting market trends, analyzing product features, and considering the customer sentiment in designing new features/products. The sentiment of unstructured data such as tweets, Facebook comments, and web reviews is calculated by using the polarity and intensity of the words, whereas polarity indicates positive or negative sentiment, and intensity indicates the strength of polarity. In this paper, a comparative study of sentiment analysis performance and accuracy between all bigrams and selective adverb/adjective bigrams is done. The outcome of this research will serve as a metric for both academia and industry to implement sentiment analysis projects.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
3.
go back to reference Hemalatha I, Saradhi Varma GP, Govardhan A (2012) Preprocessing the informal text for efficient sentiment analysis. Int J Emerg Trends Technol Comput Sci Hemalatha I, Saradhi Varma GP, Govardhan A (2012) Preprocessing the informal text for efficient sentiment analysis. Int J Emerg Trends Technol Comput Sci
9.
go back to reference Palanisamy P, Yadav V, Elchuri SH (2013) Simple and practical lexicon based approach to sentiment analysis. Assoc Comput Linguist 543–548 Palanisamy P, Yadav V, Elchuri SH (2013) Simple and practical lexicon based approach to sentiment analysis. Assoc Comput Linguist 543–548
12.
go back to reference Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27:130–135 Song YY, Lu Y (2015) Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 27:130–135
13.
go back to reference Gatti L, Guerini M, Turchi M (2016) SentiWords: deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Trans Affect Comput 7(4):409–421CrossRef Gatti L, Guerini M, Turchi M (2016) SentiWords: deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Trans Affect Comput 7(4):409–421CrossRef
Metadata
Title
Comparative Analysis of Sentiment Analysis Between All Bigrams and Selective Adverb/Adjective Bigrams
Authors
Mounicasri Valavala
Hemalatha Indukuri
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-3828-5_69