STRESS PREDICTION USING MACHINE LEARNING

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 TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system todetect the strength of stress and relaxation expressed in social media text messages. TensiStrength 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. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength 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 resultPrecision 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, TensiStrength, wordsense disambiguation.