Taking advantage of the popularity of the web, online marketplaces such as Ebay (.com), advertisements (ads for short) websites such as Craigslist(.org), and commercial websites such as Carmax(.com) (allow users to) post ads on a variety of products and services. Instead of browsing through numerous websites to locate ads of interest, web users would benefit from the existence of a single, fully integrated database (DB) with ads in multiple domains, such as Cars-for-Sale and Job-Postings, populated from various online sources so that ads of interest could be retrieved at a centralized site. Since existing ads websites impose their own structures and formats for storing and accessing ads, generating a uniform, integrated ads repository is not a trivial task. The challenges include (i) identifying ads domains, (ii) dealing with the diversity in structures of ads in various ads domains, and (iii) analyzing data with different meanings in each ads domain. To handle these problems, we introduce ADEx, a tool that relies on various machine learning approaches to automate the process of extracting (un-/semi-/fully- structured) data from online ads to create ads records archived in an underlying DB through domain classification, keyword tagging, and identification of valid attribute values. Experimental results generated using a dataset of 18,000 online ads originated from Craigslist, Ebay, and KSL(.com) show that ADEx is superior in performance compared with existing text classification, keyword labeling, and data extraction approaches. Further evaluations verify that ADEx either outperforms or performs at least as good as current state-of-the-art information extractors in mapping data from unstructured or (semi-)structured sources into DB records.

Taking advantage of the Web, many advertisements (ads for short) websites, which aspire to increase client's transactions and thus profits, offer searching tools which allow users to (i) post keyword queries to capture their information needs or (ii) invoke form-based interfaces to create queries by selecting search options, such as a price range, filled-in entries, check boxes, or drop-down menus. These search mechanisms, however, are inadequate, since they cannot be used to specify a natural-language query with rich syntactic and semantic content, which can only be handled by a question answering (QA) system. Furthermore, existing ads websites are incapable of evaluating arbitrary Boolean queries or retrieving partiallymatched answers that might be of interest to the user whenever a user's search yields only a few or no results at all. In solving these problems, we present a QA system for ads, called CQAds, which (i) allows users to post a natural-language question Q for retrieving relevant ads, if they exist, (ii) identifies ads as answers that partially-match the requested information expressed in Q, if insufficient or no answers to Q can be retrieved, which are ordered using a similarity-ranking approach, and (iii) analyzes incomplete or ambiguous questions to perform the "best guess" in retrieving answers that "best match" the selection criteria specified in Q. CQAds is also equipped with a Boolean model to evaluate Boolean operators that are either explicitly or implicitly specified in Q, i.e., with or without Boolean operators specified by the users, respectively. CQAds is easy to use, scalable to all ads domains, and more powerful than search tools provided by existing ads websites, since its query-processing strategy retrieves relevant ads of higher quality and quantity. We have verified the accuracy of CQAds in retrieving ads on eight ads domains and compared its ranking strategy with other well-known ranking approaches.

Review websites, such as Epinions.com, which offer users a platform to share their opinions on diverse products and services, provide a valuable source of opinion-rich information. Browsing through archived reviews to locate different opinions on a product or service, however, is a time-consuming and tedious task, and in most cases, the large amount of available information is difficult for users to absorb. To facilitate the process of synthesizing opinions expressed in reviews on a product or service P specified in a user query/question Q, we introduce QMSS, a query-based multi-document sentiment summarizer. QMSS creates a summary for Q, which either reflects the general opinions on P or is tailored to specific facets (i.e., features) and/or sentiment of P as specified in Q. QMSS (i) identifies the facets addressed in reviews retrieved for Q, (ii) employs a sentence-based, sentiment classifier to determine the polarity of each sentence in each review, and (iii) clusters sentences in reviews according to the facets captured in the sentences, which are identified using a keyword-label extraction algorithm. This process dictates which sentences in the reviews should be included in the summary for Q. Empirical studies have verified that QMSS is highly effective in generating summaries that satisfy users' information needs and ranks on top among the state-of-the-art query-based multi-document sentiment summarizers.

These days web users searching for opinions expressed by others on a particular product or service PS can turn to review repositories, such as Epinions.com or Imdb.com. While these repositories often provide a high quantity of reviews on PS, browsing through archived reviews to locate different opinions expressed on PS is a time-consuming and tedious task, and in most cases, a very labor-intensive process. To simplify the task of identifying reviews expressing positive, negative, and neutral opinions on PS, we introduce a simple, yet effective sentiment classifier, denoted SentiClass, which categorizes reviews on PS using the semantic, syntactic, and sentiment content of the reviews. To speed up the classification process, SentiClass summarizes each review to be classified using eSummar, a single-document, extractive, sentiment summarizer proposed in this paper, based on various sentence scores and anaphora resolution. SentiClass (eSummar, respectively) is domain and structure independent and does not require any training for performing the classification (summarization, respectively) task. Empirical studies conducted on two widely-used datasets, Movie Reviews and Game Reviews, in addition to a collection of Epinions.com reviews, show that SentiClass (i) is highly accurate in classifying summarized or full reviews and (ii) outperforms well-known classifiers in categorizing reviews.

Question-Answering (QA) service is a growing area of research study, and commercial QA systems have recently been developed. We are motivated to provide complementary QA service that answers questions in advertisements (ads). These days with almost all businesses online, potential buyers who search for merchandises to purchase through the Internet are also flourishing. When a Web user looks for products online, he may have many questions on his mind for which he would be eager to receive answers prior to finalizing his purchasing decision. Although some ads Web sites are complemented with FAQs, their QA services either are non-existent or do not provide answers to inquires in real time automatically. We address these problems by answering user's questions such as "Which is the cheapest car?", "Are there any entry-level, software developer positions?", etc., spontaneously in real time. Existing general-purpose QA systems, such as Ask.com, provide answers to a user's question Q in a list format. A more sophisticated approach is to order the answers to Q according to their degrees of relevance to Q. We propose a QA system which deals with the challenge of interpreting users' questions and retrieves correct, as well as partially-matched ranked, answers. Experimental results have verified that the proposed QA system is highly accurate in answering users' questions on car ads.