1. A system for context-based sentiment analysis, comprising: one or more processors configured to: obtain a piece of comment data that is initially determined to be associated with a positive sentiment, a negative sentiment, a neutral sentiment, or a mixed sentiment, the piece of comment data being included in a comment; determine a context sentiment type of an associated context related to the piece of comment data, the associated context being distinct from the comment and including text that is created at a different time than the piece of comment data, and the determination being made with reference to at least a set of context features, wherein the context sentiment type indicates that the associated context is positive sentiment-entailing, negative sentiment-entailing, or neutral, wherein the determining of the context sentiment type of the associated context related to the piece of comment data comprises to: determine whether the context sentiment type of the associated context related to the piece of comment data is positive sentiment-entailing; and in response to a determination that the context sentiment type is determined to be positive sentiment-entailing, classify the piece of comment data includes using a positive model-based comment classifier, comprising to: perform an initial classification; and in response to a determination that the initial classification is positive and a positive context model-based comment classification of the positive model-based comment classifier is not-positive, determine a final classification to be neutral; and classify the piece of comment data, the classification being based at least in part on the piece of comment data and the context sentiment type; and one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions.
1. A system for context-based sentiment analysis, comprising: one or more processors configured to: obtain a piece of comment data that is initially determined to be associated with a positive sentiment, a negative sentiment, a neutral sentiment, or a mixed sentiment, the piece of comment data being included in a comment; determine a context sentiment type of an associated context related to the piece of comment data, the associated context being distinct from the comment and including text that is created at a different time than the piece of comment data, and the determination being made with reference to at least a set of context features, wherein the context sentiment type indicates that the associated context is positive sentiment-entailing, negative sentiment-entailing, or neutral, wherein the determining of the context sentiment type of the associated context related to the piece of comment data comprises to: determine whether the context sentiment type of the associated context related to the piece of comment data is positive sentiment-entailing; and in response to a determination that the context sentiment type is determined to be positive sentiment-entailing, classify the piece of comment data includes using a positive model-based comment classifier, comprising to: perform an initial classification; and in response to a determination that the initial classification is positive and a positive context model-based comment classification of the positive model-based comment classifier is not-positive, determine a final classification to be neutral; and classify the piece of comment data, the classification being based at least in part on the piece of comment data and the context sentiment type; and one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions. 2. The system of claim 1 , wherein the associated context is supplied by a first user, and the comment is supplied by a second user distinct from the first user.