Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a given document with respect to a given aspect entity. While neural network architectures have been successful in predicting the overall polarity of sentences, aspect-specific sentiment analysis still remains as an open problem. In this paper, we propose a novel method for integrating aspect information into the neural model. More specifically, we incorporate aspect information into the neural model by modeling word-aspect relationships. Our novel model, (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term. This ameliorates the flaws of other state-of-the-art models that utilize naive concatenations to model word-aspect similarity. Instead, our model adopts circular convolution and circular correlation to model the similarity between aspect and words and elegantly incorporates this within a differentiable neural attention framework. Finally, our model is end-to-end differentiable and highly related to convolution-correlation (holographic like) memories. Our proposed neural model achieves state-of-the-art performance on benchmark datasets, outperforming ATAE-LSTM by @math on average across multiple datasets.
Sentiment analysis is a long standing problem in the field of NLP. Simply speaking, this task can be often interpreted as a multi-class (or binary) classification problem in which many decades of research have been dedicated to building features and running them through Support Vector Machine (SVM) classifiers. These traditional features include from sentiment lexicons @cite_4 @cite_6 to ngram features or parse-tree features @cite_19 @cite_0 .
@cite_0 @cite_19 @cite_4 @cite_6
1572786359 2030866871 2012070465 2109634664
Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis. Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents. Document-level sentiment classification aims to automate the task of classifying a textual review, which is given on a single topic, as expressing a positive or negative sentiment. In general, supervised methods consist of two stages: (i) extraction selection of informative features and (ii) classification of reviews by using learning models like Support Vector Machines (SVM) and Nai@?ve Bayes (NB). SVM have been extensively and successfully used as a sentiment learning approach while Artificial Neural Networks (ANN) have rarely been considered in comparative studies in the sentiment analysis literature. This paper presents an empirical comparison between SVM and ANN regarding document-level sentiment analysis. We discuss requirements, resulting models and contexts in which both approaches achieve better levels of classification accuracy. We adopt a standard evaluation context with popular supervised methods for feature selection and weighting in a traditional bag-of-words model. Except for some unbalanced data contexts, our experiments indicated that ANN produce superior or at least comparable results to SVM's. Specially on the benchmark dataset of Movies reviews, ANN outperformed SVM by a statistically significant difference, even on the context of unbalanced data. Our results have also confirmed some potential limitations of both models, which have been rarely discussed in the sentiment classification literature, like the computational cost of SVM at the running time and ANN at the training time. Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.