This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction.
Our work is also related to transfer learning or domain adaptation @cite_4 . In the sentiment classification context, trained sentiment classifiers for the target domain using various mixes of labeled and unlabeled reviews. proposed to first find some common or pivot features from the source and the target, and then identify correlated features with the pivot features. The final classifier is built using the combined features. built a meta-classifier (called CLF) using the outputs of each base classifier constructed in each domain. Other works along similar lines include @cite_9 @cite_15 @cite_16 @cite_31 @cite_10 @cite_5 @cite_4 @cite_30 @cite_8 @cite_20 @cite_25 . Additional details about these and other related works can be found in @cite_27 . However, as we discussed in the introduction, these methods do not focus on the ability to accumulate learned knowledge and leverage it in new learning in a lifelong manner.
@cite_30 @cite_4 @cite_15 @cite_8 @cite_9 @cite_27 @cite_5 @cite_31 @cite_16 @cite_10 @cite_25 @cite_20
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In this work, we attempt to tackle domain-transfer problem by combining old-domain labeled examples with new-domain unlabeled ones. The basic idea is to use old-domain-trained classifier to label some informative unlabeled examples in new domain, and retrain the base classifier over these selected examples. The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain. A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. We describe a sentiment classification method that is applicable when we do not have any labeled data for a target domain but have some labeled data for multiple other domains, designated as the source domains. We automatically create a sentiment sensitive thesaurus using both labeled and unlabeled data from multiple source domains to find the association between words that express similar sentiments in different domains. The created thesaurus is then used to expand feature vectors to train a binary classifier. Unlike previous cross-domain sentiment classification methods, our method can efficiently learn from multiple source domains. Our method significantly outperforms numerous baselines and returns results that are better than or comparable to previous cross-domain sentiment classification methods on a benchmark dataset containing Amazon user reviews for different types of products. With the aim to deal with sentiment-transfer problem, we proposed a novel approach, which integrates the sentiment orientations of documents into the graph-ranking algorithm. We apply the graph-ranking algorithm using the accurate labels of old-domain documents as well as the "pseudo" labels of new-domain documents. Experimental results show that proposed algorithm could improve the performance of baseline methods dramatically for sentiment transfer. This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed classifiers, CRF classifier and SVM classifier. Experiments show that the injection of morphological information improves the performance of the word polarity detection. To utilize syntactic structures, we annotate structural trios to represent relations between sentence segments. Experiments show that considering structural trios is useful for sentence opinion analysis. The best f-score achieves 0.77 for opinion word extraction, 0.62 for opinion word polarity detection, 0.80 for opinion sentence extraction, and 0.54 for opinion sentence polarity detection. Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95 on the movie review data and an average of 90 on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning. Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.