Search (4 results, page 1 of 1)

  • × author_ss:"Paltoglou, G."
  • × year_i:[2010 TO 2020}
  1. Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010) 0.04 
    0.03661915 = product of:
  0.0732383 = sum of:
    0.057547275 = weight(_text_:social in 4200) [ClassicSimilarity], result of:
      0.057547275 = score(doc=4200,freq=4.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.3115296 = fieldWeight in 4200, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4200)
    0.015691021 = product of:
      0.031382043 = sum of:
        0.031382043 = weight(_text_:22 in 4200) [ClassicSimilarity], result of:
          0.031382043 = score(doc=4200,freq=2.0), product of:
            0.16222252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046325076 = queryNorm
            0.19345059 = fieldWeight in 4200, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4200)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    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 behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. 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 optimized 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.
    Date
    22. 1.2011 14:29:23
  2. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012) 0.02 
    0.024918701 = product of:
  0.099674806 = sum of:
    0.099674806 = weight(_text_:social in 4972) [ClassicSimilarity], result of:
      0.099674806 = score(doc=4972,freq=12.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.5395851 = fieldWeight in 4972, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4972)
  0.25 = coord(1/4)
    Abstract
    Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
  3. Paltoglou, G.: Sentiment-based event detection in Twitter (2016) 0.01 
    0.014386819 = product of:
  0.057547275 = sum of:
    0.057547275 = weight(_text_:social in 3010) [ClassicSimilarity], result of:
      0.057547275 = score(doc=3010,freq=4.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.3115296 = fieldWeight in 3010, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3010)
  0.25 = coord(1/4)
    Abstract
    The main focus of this article is to examine whether sentiment analysis can be successfully used for "event detection," that is, detecting significant events that occur in the world. Most solutions to this problem are typically based on increases or spikes in frequency of terms in social media. In our case, we explore whether sudden changes in the positivity or negativity that keywords are typically associated with can be exploited for this purpose. A data set that contains several million Twitter messages over a 1-month time span is presented and experimental results demonstrate that sentiment analysis can be successfully utilized for this purpose. Further experiments study the sensitivity of both frequency- or sentiment-based solutions to a number of parameters. Concretely, we show that the number of tweets that are used for event detection is an important factor, while the number of days used to extract token frequency or sentiment averages is not. Lastly, we present results focusing on detecting local events and conclude that all approaches are dependant on the level of coverage that such events receive in social media.
  4. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011) 0.00 
    0.004707306 = product of:
  0.018829225 = sum of:
    0.018829225 = product of:
      0.03765845 = sum of:
        0.03765845 = weight(_text_:22 in 4345) [ClassicSimilarity], result of:
          0.03765845 = score(doc=4345,freq=2.0), product of:
            0.16222252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046325076 = queryNorm
            0.23214069 = fieldWeight in 4345, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4345)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 1.2011 14:27:06

Authors

Themes