This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.

While traditional research on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author's mood, gender, age, or sentiment. Without knowing the user's intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address the problem of clustering documents along the user-desired dimension, previous work has focused on learning a similarity metric from data manually annotated with the user's intention or having a human construct a feature space in an interactive manner during the clustering process. With the goal of reducing reliance on human knowledge for fine-tuning the similarity function or selecting the relevant features required by these approaches, we propose a novel active clustering algorithm, which allows a user to easily select the dimension along which she wants to cluster the documents by inspecting only a small number of words. We demonstrate the viability of our algorithm on a variety of commonly-used sentiment datasets.2. We follow Ng et al. (2001) and employ a normalized dual form of the usual Laplacian D − S.

Unsupervised morphological analysis is the task of segmenting words into prefixes, suffixes and stems without prior knowledge of language-specific morphotactics and morpho-phonological rules. This paper introduces a simple, yet highly effective algorithm for unsupervised morphological learning for Bengali, an Indo-Aryan language that is highly inflectional in nature. When evaluated on a set of 4,110 human-segmented Bengali words, our algorithm achieves an F-score of 83%, substantially outperforming Linguistica, one of the most widely-used unsupervised morphological parsers, by about 23%.

Although it is common practice to produce only a single clustering of a dataset, in many cases text documents can be clustered along different dimensions. Unfortunately, not only do traditional text clustering algorithms fail to produce multiple clusterings of a dataset, the only clustering they produce may not be the one that the user desires. In this paper, we propose a simple active clustering algorithm that is capable of producing multiple clusterings of the same data according to user interest. In comparison to previous work on feedback-oriented clustering, the amount of user feedback required by our algorithm is minimal. In fact, the feedback turns out to be as simple as a cursory look at a list of words. Experimental results are very promising: our system is able to generate clusterings along the user-specified dimensions with reasonable accuracies on several challenging text classification tasks, thus providing suggestive evidence that our approach is viable.

Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive process of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part to the prevalence of sentimentally ambiguous reviews, where reviewers discuss both the positive and negative aspects of a product. To address this problem, we propose a semi-supervised approach to sentiment classification where we first mine the unambiguous reviews using spectral techniques and then exploit them to classify the ambiguous reviews via a novel combination of active learning, transductive learning, and ensemble learning.