Opinion mining using combinational approach for different domains

An increase in use of web produces large content of information about products. Online reviews are used to make decision by peoples. Opinion mining is vast research area in which different types of reviews are analyzed. Several issues are existing in this area. Domain adaptation is emerging issue in opinion mining. Labling of data for every domain is time consuming and costly task. Hence the need arises for model that train one domain and applied it on other domain reducing cost aswell as time. This is called domain adaptation which is addressed in this paper. Using maximum entropy and clustering technique source domains data is trained. Trained data from source domain is applied on target data to labeling purpose A result shows moderate accuracy for 5 fold cross validation and combination of source domains for Blitzer et al (2007) multi domain product dataset

SENTIMENT STRENGTH AND TOPIC RECOGNITION IN SENTIMENT ANALYSIS

Current sentiment analysis methods focus on determining the sentiment polarities (negative, neutral or positive) in users’ sentiments. However, in order to correctly classify users’ sentiments into their right polarities, the strengths of these sentiments must be considered. In addition to classifying users’ sentiments into their correct polarities, it is important to determine the sources and topics under which users’ sentiments fall. Sentiment strength helps as to understand the levels of customer satisfaction toward products and services. Sentiment topics on the other hand, helps to determine the specific product/service areas associated with user sentiments. This paper proposes two sentiment analysis approaches. First an approach which determines the sentiment strength expressed by consumers in terms of a scale (highly positive, +5 to highly negative, -5) is proposed. The approach includes a novel algorithm to compute the strength of sentiment polarity for each text by including the weights of the words used in the texts. Second, a sentiment mining approach which detects sentiment topic from text is proposed. The approach includes a sentiment topic recognition model that is based on Correlated Topics Models (CTM) with Variational Expectation-Maximization (VEM) algorithm. Finally, the effectiveness and efficiency of these models is validated using airline data from Twitter and customer review dataset from amazon.com --Abstract, p. ii

Unionization method for changing opinion in sentiment classification using machine learning

Sentiment classification aims to determine whether an opinionated text expresses a positive, negative or neutral opinion. Most existing sentiment classification approaches have focused on supervised text classification techniques. One critical problem of sentiment classification is that a text collection may contain tens or hundreds of thousands of features, i.e. high dimensionality, which can be solved by dimension reduction approach. Nonetheless, although feature selection as a dimension reduction method can reduce feature space to provide a reduced feature subset, the size of the subset commonly requires further reduction. In this research, a novel dimension reduction approach called feature unionization is proposed to construct a more reduced feature subset. This approach works based on the combination of several features to create a more informative single feature. Another challenge of sentiment classification is the handling of concept drift problem in the learning step. Users’ opinions are changed due to evolution of target entities over time. However, the existing sentiment classification approaches do not consider the evolution of users’ opinions. They assume that instances are independent, identically distributed and generated from a stationary distribution, even though they are generated from a stream distribution. In this study, a stream sentiment classification method is proposed to deal with changing opinion and imbalanced data distribution using ensemble learning and instance selection methods. In relation to the concept drift problem, another important issue is the handling of feature drift in the sentiment classification. To handle feature drift, relevant features need to be detected to update classifiers. Since proposed feature unionization method is very effective to construct more relevant features, it is further used to handle feature drift. Thus, a method to deal with concept and feature drifts for stream sentiment classification was proposed. The effectiveness of the feature unionization method was compared with the feature selection method over fourteen publicly available datasets in sentiment classification domain using three typical classifiers. The experimental results showed the proposed approach is more effective than current feature selection approaches. In addition, the experimental results showed the effectiveness of the proposed stream sentiment classification method in comparison to static sentiment classification. The experiments conducted on four datasets, have successfully shown that the proposed algorithm achieved better results and proving the effectiveness of the proposed method

Acquiring Broad Commonsense Knowledge for Sentiment Analysis Using Human Computation

While artificial intelligence is successful in many applications that cover specific domains, for many commonsense problems there is still a large gap with human performance. Automated sentiment analysis is a typical example: while there are techniques that reasonably aggregate sentiments from texts in specific domains, such as online reviews of a particular product category, more general models have a poor performance. We argue that sentiment analysis can be covered more broadly by extending models with commonsense knowledge acquired at scale, using human computation. We study two sentiment analysis problems. We start with document-level sentiment classification, which aims to determine whether a text as a whole expresses a positive or a negative sentiment. We hypothesize that extending classifiers to include the polarities of sentiment words in context can help them scale to broad domains. We also study fine-grained opinion extraction, which aims to pinpoint individual opinions in a text, along with their targets. We hypothesize that extraction models can benefit from broad fine-grained annotations to boost their performance on unfamiliar domains. Selecting sentiment words in context and annotating texts with opinions and targets are tasks that require commonsense knowledge shared by all the speakers of a language. We show how these can be effectively solved through human computation. We illustrate how to define small tasks that can be solved by many independent workers so that results can form a single coherent knowledge base. We also show how to recruit, train, and engage workers, then how to perform effective quality control to obtain sufficiently high-quality knowledge. We show how the resulting knowledge can be effectively integrated into models that scale to broad domains and also perform well in unfamiliar domains. We engage workers through both enjoyment and payment, by designing our tasks as games played for money. We recruit them on a paid crowdsourcing platform where we can reach out to a large pool of active workers. This is an effective recipe for acquiring sentiment knowledge in English, a language that is known by the vast majority of workers on the platform. To acquire sentiment knowledge for other languages, which have received comparatively little attention, we argue that we need to design tasks that appeal to voluntary workers outside the crowdsourcing platform, based on enjoyment alone. However, recruiting and engaging volunteers has been more of an art than a problem that can be solved systematically. We show that combining online advertisement with games, an approach that has been recently proved to work well for acquiring expert knowledge, gives an effective recipe for luring and engaging volunteers to provide good quality sentiment knowledge for texts in French. Our solutions could point the way to how to use human computation to broaden the competence of artificial intelligence systems in other domains as well