Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance Abstract: In this work, Sentiment Analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes and feelings expressed in text. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. Emotion mining can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences and discovers the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. The Vast majority of existing approaches to opinion feature extraction rely on mining patterns only from a single review corpus, ignoring the nontrivial disparities in word distributional characteristics of opinion features across different corpora. In this paper, we propose a novel method to identify opinion features from online reviews by exploiting the difference in opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrasting corpus). We capture this disparity via a measure called domain relevance (DR), which characterizes the relevance of a term to a text collection. Existing system: Sentiment classification model trained in one domain cannot work well in another domain. Favor supervised learning, requiring labeled corpora for training, and potentially limiting the applicability to other domains of interest. Topic/feature detection and sentiment classification are often performed separately, which ignores their mutual dependence. Without considering the mixture of topics in the text, limits the effectiveness of the mining results to users. – e.g. ‘unpredictable steering’: • Negative in automobile review • Positive in movie review Proposed system: We first extract a list of candidate opinion features from the domain review corpus by defining a set of syntactic dependence rules. For each extracted candidate feature, we then estimate its intrinsic-domain relevance (IDR) and extrinsic-domain relevance (EDR) scores on the domain-dependent and domainindependent corpora, respectively. Supervised learning model may be tuned to work well in a given domain, but the model must be retrained if it is applied to different domains. Topic modeling approaches can mine coarse-grained and generic topics or aspects, which are actually semantic feature clusters or aspects of the specific features commented on explicitly in reviews. System Requirements: Software Requirements: Operating System : Windows XP. Platform : JDK1.6. Server side : Glassfish Server 2.1, JSP, Xampp 1.7.1. Frontend : JSP. Backend : MySQL 5.1. Hardware Requirements: Processor : Pentium 4 RAM : 512 MB and above Hard Disk : 40 GB and above