UoM: Using Explicit Semantic Analysis for Classifying Sentiments
نویسندگان
چکیده
In this paper, we describe our system submitted for the Sentiment Analysis task at SemEval 2013 (Task 2). We implemented a combination of Explicit Semantic Analysis (ESA) with Naive Bayes classifier. ESA represents text as a high dimensional vector of explicitly defined topics, following the distributional semantic model. This approach is novel in the sense that ESA has not been used for Sentiment Analysis in the literature, to the best of our knowledge.
منابع مشابه
Latent Semantic Matching: Application to Cross-language Text Categorization without Alignment Information
Unsupervised object matching (UOM) is a promising approach to cross-language natural language processing such as bilingual lexicon acquisition, parallel corpus construction, and cross-language text categorization, because it does not require labor-intensive linguistic resources. However, UOM only finds one-to-one correspondences from data sets with the same number of instances in source and tar...
متن کاملJoint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models
In this work, we build an entity/event-level sentiment analysis system, which is able to recognize and infer both explicit and implicit sentiments toward entities and events in the text. We design Probabilistic Soft Logic models that integrate explicit sentiments, inference rules, and +/-effect event information (events that positively or negatively affect entities). The experiments show that t...
متن کاملEntity/Event-Level Sentiment Detection and Inference
Most of the work in sentiment analysis and opinion mining focuses on extracting explicit sentiments. Opinions may be expressed implicitly via inference rules over explicit sentiments. In this thesis, we incorporate the inference rules as constraints in joint prediction models, to develop an entity/event-level sentiment analysis system which aims at detecting both explicit and implicit sentiment...
متن کاملPresentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures
Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...
متن کاملPresentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures
Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...
متن کامل