Electronic text on internet can be used for many online activities similarly it can also be used for social movement activities. The electronic text through social movements can also be used to describe an issue, place blame, identify victims, propose a solution and appeal readers to take action on it. Texts such as these are framing documents. Framing is a unique concept in sociology & political science in which people interpret information and speak in favour or claim. Online communities are using frames on social media for their good or bad goals. Thus framing and contents in it have cumulative effect on sentiment of people which needs to be studied. Sentiment analysis explores attitudes, feelings, and expressed opinions regarding products, topics, or issues. The research presented here proposes a framework that applies statistical methods in text analytics to extend research in framing process to find sentiments expressed by people in frame. In research work, first phase is to pre-process text; it uses supervised machine learning methods that create a tone based term matrix. Second phase discover distinct patterns that characterize prominent frames by classifying the corpus into frames and non-frames. last phase aims to classify frames more specific into motivational, investigative and predictive on the basis of sentiments expressed in them so as to find out threat, cause or solution for an issue. The research presented here aims to develop a tool that will help social movement organizations and concerned authorities to portray issue and helps in organizing activities properly.

Sentiment, Tone, Frame, Context- Concept Quadruple

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