Search (3 results, page 1 of 1)

0.0022962927 = product of:
  0.0045925854 = sum of:
    0.0045925854 = product of:
      0.009185171 = sum of:
        0.009185171 = weight(_text_:a in 3540) [ClassicSimilarity], result of:
          0.009185171 = score(doc=3540,freq=22.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.21126054 = fieldWeight in 3540, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3540)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.

Type

a

0.0013847164 = product of:
  0.0027694327 = sum of:
    0.0027694327 = product of:
      0.0055388655 = sum of:
        0.0055388655 = weight(_text_:a in 4987) [ClassicSimilarity], result of:
          0.0055388655 = score(doc=4987,freq=8.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.12739488 = fieldWeight in 4987, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4987)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State-of-the-art approaches view it as an expert search or resource selection problem. We investigate the effect of content-based similarity between posts on the performance of the retrieval system. We test two different approaches for smoothing (regularizing) relevance scores of posts based on their dependencies. In the first approach, we smooth term distributions describing posts by performing a random walk over a document-term graph in which similar posts are highly connected. In the second, we directly smooth scores for posts using a regularization framework that aims to minimize the discrepancy between scores for similar documents. We then extend these approaches to consider the time interval between the posts in smoothing the scores. The idea is that if two posts are temporally close, then they are good sources for smoothing each other's relevance scores. We compare these methods with the state-of-the-art approaches in blog search that employ Language Modeling-based resource selection algorithms and fusion-based methods for aggregating post relevance scores. We show performance gains over the baseline techniques which do not take advantage of the relation between posts for smoothing relevance estimates.

Type

a

9.791424E-4 = product of:
  0.0019582848 = sum of:
    0.0019582848 = product of:
      0.0039165695 = sum of:
        0.0039165695 = weight(_text_:a in 4469) [ClassicSimilarity], result of:
          0.0039165695 = score(doc=4469,freq=4.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.090081796 = fieldWeight in 4469, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4469)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This book offers a helpful starting point in the scattered, rich, and complex body of literature on Mobile Information Retrieval (Mobile IR), reviewing more than 200 papers in nine chapters. Highlighting the most interesting and influential contributions that have appeared in recent years, it particularly focuses on both user interaction and techniques for the perception and use of context, which, taken together, shape much of today's research on Mobile IR. The book starts by addressing the differences between IR and Mobile IR, while also reviewing the foundations of Mobile IR research. It then examines the different kinds of documents, users, and information needs that can be found in Mobile IR, and which set it apart from standard IR. Next, it discusses the two important issues of user interfaces and context-awareness. In closing, it covers issues related to the evaluation of Mobile IR applications. Overall, the book offers a valuable tool, helping new and veteran researchers alike to navigate this exciting and highly dynamic area of research.