Opinion Mining Tasks Research Articles

Comparison of feature-level learning methods for mining online consumer reviews

The tasks of feature-level opinion mining usually include the extraction of product entities from consumer reviews, the identification of opinion words that are associated with the entities, and the determining of these opinions’ polarities (e.g., positive, negative, or neutral). In recent years, two major approaches have been proposed to determine opinions at the feature level: model based methods such as the one based on lexicalized Hidden Markov Model (L-HMMs), and statistical methods like the association rule mining based technique. However, little work has compared these algorithms regarding their practical abilities in identifying various types of review elements, such as features, opinions, intensifiers, entity phrases and infrequent entities. On the other hand, little attentions has been paid to applying more discriminative learning models to accomplish these opinion mining tasks. In this paper, we not only experimentally compared these methods based on a real-world review dataset, but also in particular adopted the Conditional Random Fields (CRFs) model and evaluated its performance in comparison with related algorithms. Moreover, for CRFs-based mining algorithm, we tested the role of a self-tagging process in two automatic training conditions, and further identified the ideal combination of learning functions to optimize its learning performance. The comparative experiment eventually revealed the CRFs-based method’s outperforming accuracy in terms of mining multiple review elements, relative to other methods.

Early Fusion of Low Level Features for Emotion Mining

We study the discrimination of emotions annotated in free texts at the sentence level: a sentence can either be associated with no emotion (neutral) or multiple labels of emotion. The proposed system relies on three characteristics. We implement an early fusion of grams of increasing orders transposing an approach successfully employed in the related task of opinion mining. We apply a filtering process that consists in extracting frequent n-grams and making use of the Shannon’s entropy measure to respectively maintain dictionaries at balanced sizes and keep emotion specific features. Finally the overall system is implemented as a 2-step decision process: a first classifier discriminates between neutral and emotion bearing sentences, then one classifier per emotion is applied on emotion bearing sentences. The final decision is given by the classifier holding the maximum confidence. Results obtained on the testing set are promising.

A Dynamic Web Mining Framework for E-Learning Recommendations using Rough Sets and Association Rule Mining

wide web is a huge information source, broadly used for learning now-a-days due to flexibility of time, sharing of learning resources and infrastructure etc., Most of web based learning system lacks expert-learner interaction, assessment of user activities and learners are getting drowned by huge number of web pages in the learning web site and they find difficulties in choosing suitable materials relevant to their interest. This work attempts to engage e-learners at an early stage of learning by providing navigation recommendations. E-learning personalization is done by mining the web usage data like recent browsing histories of learners of similar interest. The proposed method uses upper approximation based rough set clustering and dynamic all k th order association rule mining using apriori for personalizing e-learners by providing learning shortcuts. The essence of combing association rule and clustering is that, using clustered access patterns can reduce the data set size for association rule mining task, and improves the recommendation accuracy.

OCA: Opinion corpus for Arabic

AbstractSentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research.

Application-independent feature construction based on almost-closedness properties

Feature construction has been studied extensively, including for 0/1 data samples. Given the recent breakthroughs in closedness-related constraint-based mining, we are considering its impact on feature construction for classification tasks. We investigate the use of condensed representations of frequent itemsets based on closedness properties as new features. These itemset types have been proposed to avoid set counting in difficult association rule mining tasks, i.e. when data are noisy and/or highly correlated. However, our guess is that their intrinsic properties (say the maximality for the closed itemsets and the minimality for the δ-free itemsets) should have an impact on feature quality. Understanding this remains fairly open, and we discuss these issues thanks to itemset properties on the one hand and an experimental validation on various data sets (possibly noisy) on the other hand.

Pattern-Based Opinion Mining for Stock Market Trend Prediction

Stock market reports in on-line news are widely used by amateurs to make quick investment decisions. Financial analysts often give opinions about trends of stock markets based on past and present economic event indicators. These opinions commonly appear in text form and are abundant over the Internet. It is tedious and time consuming for users to browse through such text manually let alone to understand the embedded opinions. To overcome this shortcoming, automatic trend predication methods have been proposed. Under conventional methods, reports are represented using bag of words and trend prediction is treated as a 3-way trend classification problem, i.e. trend as 'up', 'down' or 'stable'. In this paper, we propose a new pattern-based opinion mining method for market trend predication. Experiments show that (1) pattern-based classification is more effective than its word-based counterpart for feature representation; and (2) opinion mining outperforms event-based classification for trend predication. The task of opinion mining gets more difficult when the users are exposed to opinions from more than one analyst. The question becomes whose opinions should he/she trust? This lays down our second research objective, i.e. to study different opinion incorporation strategies. Intuitively, one would trust the opinion supported by the majority. However, we show that on the contrary, the user is better off trusting the most credible analyst.

CoMMA: a framework for integrated multimedia mining using multi-relational associations

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. On the basis of this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multi-relational association rule mining where the relations exist between image-based features, and textual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images.In this paper we also present a multi-relational extension to the FP-tree algorithm to accomplish the association rule mining task effectively. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our auto-annotation experiments on different test sets. Another contribution of this paper is highlighting a need to develop robust evaluation metrics for the image annotation task. We propose several applicable scoring techniques and then evaluate the performance of the different algorithms to study the utility of these techniques. A detailed analysis of the datasets used and the performance results are presented to conclude the paper.