Sentiment Severity on Location-Based Social Network (LBSN) Data of Natural disasters
K Shyamala1, Vijay Kumar Kannan2, Sheran Dass. D3
1Dr. K Shyamala*, Associate Professor, Department of Computer Science, Dr.Ambedkar Govt. Arts College, Vyasarpadi, Chennai, India.
2Vijay Kumar Kannan, Research Scholar, Department of computer science school of computing sciences, VELS Institute of Science Technology and Advanced Studies, Pallavaram, Chennai, India.
3Sheran Dass. D, M.S, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, USA.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4219-4224 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6631018520/2020©BEIESP | DOI: 10.35940/ijrte.E6631.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media emerged as one of the key components to reach disaster affected people, as they supplement planning and operational coordination. Sentiment analysis was expended to identify, extract or characterize subjective information, such as opinions, expressed in a tweet. The sentiment expressed is analyzed and is classified as positive or negative sentiment, which is not versatile enough to capture the exact sentiment conveyed by the user. Opinion mining is a machine learning process used to extract information conveyed by the user in the form of text. In this paper, the lexical analysis to sentiment analysis of twitter data is employed. Conventionally, the sentiment is conveyed using the polarity of the data but in this paper, sentiment intensity is employed to convey the sentiments. Performing sentiment analysis on tweets gives us the sentiment intensity conveyed by the user, which in turn is used to calculate the severity of the disaster event specified by the user. Further, it is also used to classify the tweets based on their severity. This paper proposes a methodology to extract relevant sentiment information from Location Based Social Network (LBSN) and suggests a unique scale to classify this information to help disaster management authority.
Keywords: Sentiment Analysis, Unsupervised Learning, Natural Language Processing, Lexical Analysis, Disaster Management, Location Based Social Network.
Scope of the Article: Natural Language Processing.