Document (#43615)

Author

Bi, Y.

Title

Sentiment classification in social media data by combining triplet belief functions

Source

Journal of the Association for Information Science and Technology. 73(2022) no.7, S.968-991

Year

2022

Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

Content

Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24605.

Theme

Inhaltsanalyse

Similar documents (content)

1. Abdi, A.; Shamsuddin, S.M.; Aliguliyev, R.M.: QMOS: Query-based multi-documents opinion-oriented summarization (2018) 0.23

   0.23313065 = sum of:
     0.23313065 = product of:
       0.97137773 = sum of:
         0.035063285 = weight(abstract_txt:opinion in 89) [ClassicSimilarity], result of:
           0.035063285 = score(doc=89,freq=1.0), product of:
             0.09328428 = queryWeight, product of:
               1.0362741 = boost
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.013097179 = queryNorm
             0.3758756 = fieldWeight in 89, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
         0.03649204 = weight(abstract_txt:opinions in 89) [ClassicSimilarity], result of:
           0.03649204 = score(doc=89,freq=1.0), product of:
             0.095801465 = queryWeight, product of:
               1.0501624 = boost
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.013097179 = queryNorm
             0.38091314 = fieldWeight in 89, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
         0.037056316 = weight(abstract_txt:orientation in 89) [ClassicSimilarity], result of:
           0.037056316 = score(doc=89,freq=1.0), product of:
             0.09678653 = queryWeight, product of:
               1.0555477 = boost
               7.000987 = idf(docFreq=109, maxDocs=44421)
               0.013097179 = queryNorm
             0.38286647 = fieldWeight in 89, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               7.000987 = idf(docFreq=109, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
         0.039331123 = weight(abstract_txt:method in 89) [ClassicSimilarity], result of:
           0.039331123 = score(doc=89,freq=4.0), product of:
             0.07993205 = queryWeight, product of:
               1.3565814 = boost
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.013097179 = queryNorm
             0.49205697 = fieldWeight in 89, product of:
               2.0 = tf(freq=4.0), with freq of:
                 4.0 = termFreq=4.0
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
         0.041878596 = weight(abstract_txt:combination in 89) [ClassicSimilarity], result of:
           0.041878596 = score(doc=89,freq=1.0), product of:
             0.13230558 = queryWeight, product of:
               1.7453176 = boost
               5.787965 = idf(docFreq=369, maxDocs=44421)
               0.013097179 = queryNorm
             0.31652933 = fieldWeight in 89, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               5.787965 = idf(docFreq=369, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
         0.78155637 = weight(abstract_txt:sentiment in 89) [ClassicSimilarity], result of:
           0.78155637 = score(doc=89,freq=8.0), product of:
             0.6712753 = queryWeight, product of:
               6.8092046 = boost
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.013097179 = queryNorm
             1.1642859 = fieldWeight in 89, product of:
               2.828427 = tf(freq=8.0), with freq of:
                 8.0 = termFreq=8.0
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.0546875 = fieldNorm(doc=89)
       0.24 = coord(6/25)
   

2. Xu, L.; Qiu, J.: Unsupervised multi-class sentiment classification approach (2019) 0.23

   0.23053022 = sum of:
     0.23053022 = product of:
       1.1526511 = sum of:
         0.014694401 = weight(abstract_txt:results in 3) [ClassicSimilarity], result of:
           0.014694401 = score(doc=3,freq=2.0), product of:
             0.047791064 = queryWeight, product of:
               1.0489596 = boost
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.013097179 = queryNorm
             0.30747172 = fieldWeight in 3, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.0625 = fieldNorm(doc=3)
         0.038927715 = weight(abstract_txt:method in 3) [ClassicSimilarity], result of:
           0.038927715 = score(doc=3,freq=3.0), product of:
             0.07993205 = queryWeight, product of:
               1.3565814 = boost
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.013097179 = queryNorm
             0.4870101 = fieldWeight in 3, product of:
               1.7320508 = tf(freq=3.0), with freq of:
                 3.0 = termFreq=3.0
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.0625 = fieldNorm(doc=3)
         0.062326144 = weight(abstract_txt:classification in 3) [ClassicSimilarity], result of:
           0.062326144 = score(doc=3,freq=7.0), product of:
             0.09441334 = queryWeight, product of:
               1.8057088 = boost
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.013097179 = queryNorm
             0.6601413 = fieldWeight in 3, product of:
               2.6457512 = tf(freq=7.0), with freq of:
                 7.0 = termFreq=7.0
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.0625 = fieldNorm(doc=3)
         0.08931339 = weight(abstract_txt:combining in 3) [ClassicSimilarity], result of:
           0.08931339 = score(doc=3,freq=1.0), product of:
             0.22956036 = queryWeight, product of:
               2.8156552 = boost
               6.225004 = idf(docFreq=238, maxDocs=44421)
               0.013097179 = queryNorm
             0.38906276 = fieldWeight in 3, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.225004 = idf(docFreq=238, maxDocs=44421)
               0.0625 = fieldNorm(doc=3)
         0.94738936 = weight(abstract_txt:sentiment in 3) [ClassicSimilarity], result of:
           0.94738936 = score(doc=3,freq=9.0), product of:
             0.6712753 = queryWeight, product of:
               6.8092046 = boost
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.013097179 = queryNorm
             1.4113276 = fieldWeight in 3, product of:
               3.0 = tf(freq=9.0), with freq of:
                 9.0 = termFreq=9.0
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.0625 = fieldNorm(doc=3)
       0.2 = coord(5/25)
   

3. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.22

   0.21544962 = sum of:
     0.21544962 = product of:
       1.0772481 = sum of:
         0.010390511 = weight(abstract_txt:results in 3210) [ClassicSimilarity], result of:
           0.010390511 = score(doc=3210,freq=1.0), product of:
             0.047791064 = queryWeight, product of:
               1.0489596 = boost
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.013097179 = queryNorm
             0.21741535 = fieldWeight in 3210, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.0625 = fieldNorm(doc=3210)
         0.041705187 = weight(abstract_txt:opinions in 3210) [ClassicSimilarity], result of:
           0.041705187 = score(doc=3210,freq=1.0), product of:
             0.095801465 = queryWeight, product of:
               1.0501624 = boost
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.013097179 = queryNorm
             0.43532932 = fieldWeight in 3210, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.0625 = fieldNorm(doc=3210)
         0.098630436 = weight(abstract_txt:formulas in 3210) [ClassicSimilarity], result of:
           0.098630436 = score(doc=3210,freq=2.0), product of:
             0.13497171 = queryWeight, product of:
               1.2464986 = boost
               8.267481 = idf(docFreq=30, maxDocs=44421)
               0.013097179 = queryNorm
             0.73074895 = fieldWeight in 3210, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               8.267481 = idf(docFreq=30, maxDocs=44421)
               0.0625 = fieldNorm(doc=3210)
         0.033314724 = weight(abstract_txt:classification in 3210) [ClassicSimilarity], result of:
           0.033314724 = score(doc=3210,freq=2.0), product of:
             0.09441334 = queryWeight, product of:
               1.8057088 = boost
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.013097179 = queryNorm
             0.35286036 = fieldWeight in 3210, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.0625 = fieldNorm(doc=3210)
         0.89320725 = weight(abstract_txt:sentiment in 3210) [ClassicSimilarity], result of:
           0.89320725 = score(doc=3210,freq=8.0), product of:
             0.6712753 = queryWeight, product of:
               6.8092046 = boost
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.013097179 = queryNorm
             1.3306124 = fieldWeight in 3210, product of:
               2.828427 = tf(freq=8.0), with freq of:
                 8.0 = termFreq=8.0
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.0625 = fieldNorm(doc=3210)
       0.2 = coord(5/25)
   

4. Xing, F.Z.; Pallucchini, F.; Cambria, E.: Cognitive-inspired domain adaptation of sentiment lexicons (2019) 0.20

   0.2047921 = sum of:
     0.2047921 = product of:
       1.0239605 = sum of:
         0.040072326 = weight(abstract_txt:opinion in 104) [ClassicSimilarity], result of:
           0.040072326 = score(doc=104,freq=1.0), product of:
             0.09328428 = queryWeight, product of:
               1.0362741 = boost
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.013097179 = queryNorm
             0.4295721 = fieldWeight in 104, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.0625 = fieldNorm(doc=104)
         0.010390511 = weight(abstract_txt:results in 104) [ClassicSimilarity], result of:
           0.010390511 = score(doc=104,freq=1.0), product of:
             0.047791064 = queryWeight, product of:
               1.0489596 = boost
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.013097179 = queryNorm
             0.21741535 = fieldWeight in 104, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.0625 = fieldNorm(doc=104)
         0.046975646 = weight(abstract_txt:classifier in 104) [ClassicSimilarity], result of:
           0.046975646 = score(doc=104,freq=1.0), product of:
             0.10371158 = queryWeight, product of:
               1.0926574 = boost
               7.2471204 = idf(docFreq=85, maxDocs=44421)
               0.013097179 = queryNorm
             0.45294502 = fieldWeight in 104, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               7.2471204 = idf(docFreq=85, maxDocs=44421)
               0.0625 = fieldNorm(doc=104)
         0.033314724 = weight(abstract_txt:classification in 104) [ClassicSimilarity], result of:
           0.033314724 = score(doc=104,freq=2.0), product of:
             0.09441334 = queryWeight, product of:
               1.8057088 = boost
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.013097179 = queryNorm
             0.35286036 = fieldWeight in 104, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.0625 = fieldNorm(doc=104)
         0.89320725 = weight(abstract_txt:sentiment in 104) [ClassicSimilarity], result of:
           0.89320725 = score(doc=104,freq=8.0), product of:
             0.6712753 = queryWeight, product of:
               6.8092046 = boost
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.013097179 = queryNorm
             1.3306124 = fieldWeight in 104, product of:
               2.828427 = tf(freq=8.0), with freq of:
                 8.0 = termFreq=8.0
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.0625 = fieldNorm(doc=104)
       0.2 = coord(5/25)
   

5. Perea-Ortega, J.M.; Martín-Valdivia, M.T.; Ureña-López, L.A.; Martínez-Cámara, E.: Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches (2013) 0.19

   0.18812183 = sum of:
     0.18812183 = product of:
       0.58788073 = sum of:
         0.056670822 = weight(abstract_txt:opinion in 2045) [ClassicSimilarity], result of:
           0.056670822 = score(doc=2045,freq=2.0), product of:
             0.09328428 = queryWeight, product of:
               1.0362741 = boost
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.013097179 = queryNorm
             0.6075067 = fieldWeight in 2045, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               6.8731537 = idf(docFreq=124, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.014694401 = weight(abstract_txt:results in 2045) [ClassicSimilarity], result of:
           0.014694401 = score(doc=2045,freq=2.0), product of:
             0.047791064 = queryWeight, product of:
               1.0489596 = boost
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.013097179 = queryNorm
             0.30747172 = fieldWeight in 2045, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               3.4786456 = idf(docFreq=3724, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.041705187 = weight(abstract_txt:opinions in 2045) [ClassicSimilarity], result of:
           0.041705187 = score(doc=2045,freq=1.0), product of:
             0.095801465 = queryWeight, product of:
               1.0501624 = boost
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.013097179 = queryNorm
             0.43532932 = fieldWeight in 2045, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               6.965269 = idf(docFreq=113, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.042350076 = weight(abstract_txt:orientation in 2045) [ClassicSimilarity], result of:
           0.042350076 = score(doc=2045,freq=1.0), product of:
             0.09678653 = queryWeight, product of:
               1.0555477 = boost
               7.000987 = idf(docFreq=109, maxDocs=44421)
               0.013097179 = queryNorm
             0.4375617 = fieldWeight in 2045, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               7.000987 = idf(docFreq=109, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.031784344 = weight(abstract_txt:method in 2045) [ClassicSimilarity], result of:
           0.031784344 = score(doc=2045,freq=2.0), product of:
             0.07993205 = queryWeight, product of:
               1.3565814 = boost
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.013097179 = queryNorm
             0.39764208 = fieldWeight in 2045, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               4.4988065 = idf(docFreq=1342, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.044077422 = weight(abstract_txt:best in 2045) [ClassicSimilarity], result of:
           0.044077422 = score(doc=2045,freq=2.0), product of:
             0.09940073 = queryWeight, product of:
               1.5127953 = boost
               5.0168557 = idf(docFreq=799, maxDocs=44421)
               0.013097179 = queryNorm
             0.4434316 = fieldWeight in 2045, product of:
               1.4142135 = tf(freq=2.0), with freq of:
                 2.0 = termFreq=2.0
               5.0168557 = idf(docFreq=799, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.04080204 = weight(abstract_txt:classification in 2045) [ClassicSimilarity], result of:
           0.04080204 = score(doc=2045,freq=3.0), product of:
             0.09441334 = queryWeight, product of:
               1.8057088 = boost
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.013097179 = queryNorm
             0.43216392 = fieldWeight in 2045, product of:
               1.7320508 = tf(freq=3.0), with freq of:
                 3.0 = termFreq=3.0
               3.9921594 = idf(docFreq=2228, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
         0.31579646 = weight(abstract_txt:sentiment in 2045) [ClassicSimilarity], result of:
           0.31579646 = score(doc=2045,freq=1.0), product of:
             0.6712753 = queryWeight, product of:
               6.8092046 = boost
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.013097179 = queryNorm
             0.47044253 = fieldWeight in 2045, product of:
               1.0 = tf(freq=1.0), with freq of:
                 1.0 = termFreq=1.0
               7.5270805 = idf(docFreq=64, maxDocs=44421)
               0.0625 = fieldNorm(doc=2045)
       0.32 = coord(8/25)