The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumergenerated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a lowdimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.

With the rapid growth of the Internet, users ’ ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufactureroriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.

A huge number of informal messages are posted every day in social network sites, blogs and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behaviour to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially-oriented, designed to identify opinions about products rather than user behaviours. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de-facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimised by machine learning, SentiStrength is able to predict positive emotion with 60.6 % accuracy and negative emotion with 72.8 % accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.

Abstract We present a fully automatic and weighted dictionary to be used in topical opinion retrieval. We also define a simple topical opinion retrieval function that is free from parameters, so that the retrieval does not need any learning or tuning phase. 1

Automatic opinion recognition involves a number of related tasks, such as identifying the boundaries of opinion expression, determining their polarity, and determining their intensity. Although much progress has been made in this area, existing research typically treats each of the above tasks in isolation. In this paper, we apply a hierarchical parameter sharing technique using Conditional Random Fields for fine-grained opinion analysis, jointly detecting the boundaries of opinion expressions as well as determining two of their key attributes — polarity and intensity. Our experimental results show that our proposed approach improves the performance over a baseline that does not exploit hierarchical structure among the classes. In addition, we find that the joint approach outperforms a baseline that is based on cascading two separate components. 1

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Sentiment classification in text documents is an active data mining research topic in opinion retrieval and analysis. Different from previous studies concentrating on the development of effective classifiers, in this paper, we focus on the extraction and validation of unexpected sentences issued in sentiment classification. In this paper, we propose a general framework for determining unexpected sentences. The relevance of the extracted unexpected sentences is assessed in the context of text classification. In the experiments, we present the extraction of unexpected 1 sentences for sentiment classification within the proposed framework, and then evaluate the influence of unexpected sentences on the quality of classification tasks. The experimental results show the effectiveness and usefulness of our proposed approach.

In this research we aim to detect subjective sentences in multimodal conversations. We introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. Applying this technique to meeting speech and email conversations, we gain significant improvement over state-of-the-art approaches. Furthermore, we show that coupling the pattern-based approach with features that capture characteristics of general conversation structure yields additional improvement. 1

In this work we investigate four subjectivity and polarity tasks on spoken and written conversations. We implement and compare several pattern-based subjectivity detection approaches, including a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. We compare the use of these learned patterns with an alternative approach of using a very large set of raw pattern features. We also investigate how these pattern-based approaches can be supplemented and improved with features relating to conversation structure. Experimenting with meeting speech and email threads, we find that our novel systems incorporating varying instantiation patterns and conversation features outperform state-of-the-art systems despite having no recourse to domain-specific features such as prosodic cues and email headers. In some cases, such as when working with noisy speech recognizer output, a small set of well-motivated conversation features performs as well as a very large set of raw patterns. 1

The paper reports the University of Waterloo participation in the opinion and polarity tasks of the Blog track. The proposed method uses a lexicon built from several linguistic resources. The opinion discriminating ability of each subjective lexical unit was estimated using the Kullback-Leibler divergence. The KLD scores of subjective words occurring within fixed-size windows around instances of query terms were used in calculating document scores. The described system also used a method of identifying phrases in topic titles by matching them to Wikipedia titles. The results show that both KLD-based scores of subjective lexical units and Wikipedia-matched phrases are useful techniques that help improve opinion retrieval performance. 1.