Temporality Based Sentiment Analysis Using Linguistic Rules and Meta-Data

Research Article
  • 32 Downloads

Abstract

Internet is frequently used as a medium for exchange of information and opinion. The raw data available over the web is refined for the use in automated decision support system. Present day sentiment analysis generates Sentiscore by giving equal weightage to all the reviews without considering the temporal aspect. This somehow degrades the reliability of decision support system. In this paper, temporal sentiment analysis is proposed based on meta-data in conjunction with the linguistic context of words. An algorithm is also developed for evaluating Tempo-Sentiscore, which is a numeric value used to capture the temporal sentiment analysis. The proposed algorithm is evaluated on benchmark product review data. The experimental results based on temporality show high performance levels with precision, recall over 90%. The performance of the system demonstrates the effectiveness of the technique using human annotation. This paper also shows how the star rating is affected when Tempo-Sentiscore is considered in place of Sentiscore. This new star rating is close to the real scenario, i.e. Human Annotation.

Keywords

Sentiment analysis Temporal tagging Sentistrength Natural language processing Meta-data Linguistic rules Opinion mining 

References

  1. 1.
    Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2:1–135CrossRefGoogle Scholar
  2. 2.
    O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11(1):2Google Scholar
  3. 3.
    Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web. J Am Soc Inf Sci Technol 63:163–173CrossRefGoogle Scholar
  4. 4.
    Strapparava C, Mihalcea R (2008) Learning to identify emotions in text. In: Proceedings of the 2008 ACM symposium on applied computing, pp 1556–1560Google Scholar
  5. 5.
    Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61:2544–2558CrossRefGoogle Scholar
  6. 6.
    Marcus MP, Marcinkiewicz MA, Santorini B (1993) Building a large annotated corpus of English: The Penn Treebank. Comput Linguist 19:313–330Google Scholar
  7. 7.
    Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, pp 2200–2204Google Scholar
  8. 8.
    Thelwall M, Buckley K, Paltoglou G (2011) Sentiment in twitter events. J Am Soc Inf Sci Technol 62:406–418CrossRefGoogle Scholar
  9. 9.
    Han B, Lavie A (2004) A framework for resolution of time in natural language. ACM Trans Asian Lang Inf Process (TALIP) 3:11–32CrossRefGoogle Scholar
  10. 10.
    Chang, AX, Manning CD (2013) SUTIME: evaluation in TempEval-3. In: Atlanta, Georgia, USA, p 78Google Scholar
  11. 11.
    Dias GH, Hasanuzzaman M, Ferrari S, Mathet Y (2014) Tempowordnet for sentence time tagging. In: Proceedings of the companion publication of the 23rd international conference on World wide web companion, pp 833–838Google Scholar
  12. 12.
    Razavi AH, Matwin S, De Koninck J, Amini RR (2014) Dream sentiment analysis using second order soft co-occurrences (SOSCO) and time course representations. J Intell Inf Syst 42:393–413Google Scholar
  13. 13.
    Fukuhara T, Nakagawa H, Nishida T (2007) Understanding sentiment of people from news articles: temporal sentiment analysis of social events. In: ICWSMGoogle Scholar
  14. 14.
    Zhu Y, Newsam S (2016) Spatio-temporal Sentiment hotspot detection using geotagged photos. arXiv preprint arXiv:1609.06772
  15. 15.
    Song Z, Xia J (2016) Spatial and temporal sentiment analysis of twitter data. In: European handbook of crowdsourced geographic information, pp 205–221Google Scholar
  16. 16.
    Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Procedia Comput Sci 17:26–32CrossRefGoogle Scholar
  17. 17.
    Agarwal B, Mittal N (2016) Prominent feature extraction for sentiment analysis. vol 2. Socio-Affective Computing, Springer International PublishingGoogle Scholar
  18. 18.
    Abdul-Mageed M, Diab M, Kubler S (2013) SAMAR: subjectivity and sentiment analysis for Arabic social media. Comput Speech Lang 28:20–37CrossRefGoogle Scholar
  19. 19.
    Xu J, Cheng C (2017) A sentiment analysis model based on temporal characteristics of travel blogs. Data Anal Knowl Discov 1:87–95Google Scholar
  20. 20.
    Gurini DF, Gasparetti F, Micarelli A, Sansonetti G (2018) Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Gener Comput Syst 78:430–439CrossRefGoogle Scholar
  21. 21.
    Ganesan K, Zhai C (2012) Opinion-based entity ranking. Inf Retr 15:116–150CrossRefGoogle Scholar
  22. 22.
    Robertson S (2001) Evaluation in information retrieval. In: Lectures on information retrieval, vol 1980. Springer, pp 81–92Google Scholar

Copyright information

© The National Academy of Sciences, India 2018

Authors and Affiliations

  1. 1.JUITWaknaghatIndia

Personalised recommendations