Detection and Analysis of Stress using Machine Learning Techniques
Reshma Radheshamjee Baheti1, Supriya Kinariwala2
1Ms. Reshma Radheshamjee Baheti*, M Tech Department of CSE, MIT Aurangabad, Maharashtra, India.
2Mrs. Supriya Kinariwala, Professor, Department of CSE.MIT Aurangabad, Maharashtra, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 335-342 | Volume-9 Issue-1, October 2019 | Retrieval Number: F8573088619/2019©BEIESP | DOI: 10.35940/ijeat.F8573.109119
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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: Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter. This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using Tensi Strength framework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. Tensi Strength is a system to detect the strength of stress and relaxation expressed in social media text messages. Tensi Strength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. Tensi Strength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of Tensi Strength depends on the nature of the tweets. In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or “senses.” In the pre-processing part of-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better result Precision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets.
Keywords: Stress Detection, Data Mining, Tensi Strength, word sense disambiguation.