An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

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Mining and summarizing customer reviews

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A system, method, and computer program product for determining the emotional content of an electronic correspondence to route or prioritize the information, to set the expectations of a customer support worker, to flag those workers who are using inappropriate language with the customer, or to determine another best course to send the correspondence. In a preferred embodiment, a customer sends an electronic correspondence to a company via email. Emotionally charged words or symbols in each sentence are detected. The message is then given an emotional ranking which is used to determine what future action is most appropriate for the correspondence.

Method for routing electronic correspondence based on the level and type of emotion contained therein

We present a multi-document summarizer, MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We describe two new techniques, a centroid-based summarizer, and an evaluation scheme based on sentence utility and subsumption. We have applied this evaluation to both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.

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Centroid-based summarization of multiple documents

Human-quality text summarization systems are di cult to design, and even more di cult to evaluate, in part because documents can di er along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods. The potential linguistic ones were derived from an analysis of news-wire summaries. To evaluate these features we use a normalized version of precision-recall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical results showing the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries.

Summarizing text documents: sentence selection and evaluation metrics

This paper discusses a text extraction approach to multi-document summarization that builds on single-document summarization methods by using additional, available information about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for different genres, corpora characteristics and user requirements.

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Multi-document summarization by sentence extraction

Disclosed is a computer-assisted method for finding duplicate or near-duplicate documents or text spans within a document collection by using high-discriminability text fragments. Distinctive features of the documents or text spans are identified. For each pair of documents or text spans with at least one distinctive feature in common, the distinctive features of each document or text span are compared to determine whether the pair is duplicates or near-duplicates. An apparatus for performing this computer-assisted method is also disclosed.

Method and apparatus for efficient identification of duplicate and near-duplicate documents and text spans using high-discriminability text fragments

The method of recognizing the language of a single word as to spelling and grammar correction (e.g., identifying the appropriate language resources on a document, paragraph, sentence or even individual word basis), the automatic invocation of transliteration software based on the language of the words (e.g., automatic ASCII to Kanji substitution without requiring the user to explicitly switch into a Kanji mode), the automatic invocation of appropriate machine translation tools when the document's language is different from the user's native tongue(s), the use of document language identification to eliminate from database or web search results any documents which are not written in the user's native language and the automatic identification of user-appropriate languages for the user interface.

Method for identifying the language of individual words

Disclosed is a computer-assisted method for classifying a text document according to emotion and affect. A score is assigned to each affect term in the document. An affect score is computed for the document from the scores for each affect term. The document is then classified in accordance with the affect score. An apparatus for performing the computer-assisted method is also disclosed.

Mark Kantrowitz

Papers

Summarizing text documents: sentence selection and evaluation metrics

Multi-document summarization by sentence extraction

Method and apparatus for efficient identification of duplicate and near-duplicate documents and text spans using high-discriminability text fragments

Method for identifying the language of individual words

Method and apparatus for analyzing affect and emotion in text