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Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations

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Abstract

The application of sentiment analysis, also known as opinion mining, is more difficult in Chinese than in Indo-European languages, due to the compounding nature of Chinese words and phrases, and relatively lack of reliable resources in Chinese. This study used seed words, Chinese morphemes, which are mono-syllabic characters that function as individual words or be combined to create Chinese words and phrases, to classify movie reviews found on Yahoo! Taiwan. We utilized higher Pointwise Mutual Information (PMI) collocations, which consist of selected morpheme-level compounded features to build classifiers. The contributions of this study include the following: (Bird 2006) proposing a method of generating domain-dependent Chinese morphemes directly from large data set without any predefined sentimental resources; (Bradley and Lang 1999) building morpheme-based classifiers applicable in various movie genres, and shown to produce better results than other classifiers based on keywords (NTUSD and HowNet) or feature selection (TFIDF); (Church and Hanks in Computational linguistics, 16(1), 22-29 1990) identifying compounds that have different semantic polarities depending on contexts.

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Notes

  1. A Part-Of-Speech Tagger (P.O.S Tagger) is software that reads text and designates each word as a part of speech (and other token), such as noun, verb, adjective. The Part-of-speech tools from SINICA CKIP are available at http://ckipsvr.iis.sinica.edu.tw/

  2. Collected Taiwan Yahoo!Movies Corpus with P.O.S Tags from CKIP, https://github.com/fychao/ChineseMovieReviews

  3. Simplified/traditional Chinese conversion tables include parallel translation of common words/phrases in Taiwan, China, Hong Kong, and Singapore, and can be retrieved from following link: http://svn.wikimedia.org/svnroot/mediawiki/trunk/phase3/includes/ZhConversion.php

  4. Scikit-learn v0.12 http://scikit-learn.org/stable/

  5. Natural Language Toolkit 2.0 https://github.com/nltk

  6. Ten folding cross-validation is a process that chunks training dataset into 10 equal-lot of subsets, and then uses one subset for testing and others for training sequentially. Therefore, the validation process involves 10 iterations of training and testing procedures.

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Acknowledgments

The authors would like to thank the National Science Council, Taiwan, for financially supporting this research under Contract No. NSC 101-2410-H-004-015-MY3).

Author information

Authors and Affiliations

  1. Department Management Information Systems, National Cheng Chi University, 64, Sec.2, Chihnan Road, Wenshan District, Taipei, Taiwan, Republic of China

    Heng-Li Yang & August F. Y. Chao

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  1. Heng-Li Yang
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  2. August F. Y. Chao
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Corresponding author

Correspondence to Heng-Li Yang.

Appendixes

Appendixes

1.1 Appendix I movie genres

Number of total collected opinions from Yahoo!Movies Taiwan is 127,424 with 5-star-ranked opinions including 4,631,482 words in 18 movie genres. Note that one opinion might belong to one or more movie genres at the same time.

Genres

Rank1

Rank2

Rank3

Rank4

Rank5

Category

A = 奇幻 Fantasy

1,870

700

979

1,433

4,137

Group 1

B = 科幻 Science Fiction

2,151

857

1,403

2,323

9,657

C = 犯罪 Crime

1,116

326

535

934

2,769

Group 2

F = 動作Action

7,741

2,822

4,535

7,550

27,690

D = 劇情 Drama

8,659

2,799

4,334

7,577

33,492

Group 3

E = 溫馨/家庭 Romance/Family

315

134

250

481

2,798

H = 愛情Love Story

3,628

1,078

1,599

2,833

13,455

P = 動畫Animation

405

193

367

779

4,534

Group 6

Q = 喜劇Comedy

2,097

855

1,323

2,463

8,250

I = 冒險 Adventure

4,023

1,301

1,893

2,818

8,414

K = 恐怖 Terror

3,261

712

1,019

1,454

2,734

Group 5

R = 懸疑/驚悚 Mystery/Thriller

6,343

1,881

2,825

4,456

11,056

G = 勵志Inspiring

148

62

95

217

1,654

Group 4

J = 歷史/傳記 History/Biography

623

203

316

458

1,710

L = 戰爭War

888

306

468

826

4,189

M = 音樂/歌舞 Music/Dance

415

172

291

583

4,094

N = 紀錄片Documentary

647

17

43

51

878

O = 武俠 Martial Arts

160

60

112

182

436

Total: 260,720

44,490

14,478

22,387

37,418

141,947

 
 

17 %

6 %

9 %

14 %

54 %

 

Counting

58,968

22,387

179,365

 
 

22.6 %

8.6 %

68.8 %

 

1.2 Appendix II the pilot experiment

In a pilot experiment, we used 40,000 randomly selected opinions as training set from those pre-defined wordlists, NTUSD and HowNet, to build SVM classifiers. We kept those words their original sentiment orientation. That is, we applied positive wordlists for positive classifiers, applied negative wordlists for negative classifiers. The results show that this approach is not adequate for general purpose classifiers, because all statistical data are quite low except NTUSD negative wordlist.

Use keyword list to build SVM Model

Result

Use: HOWNET positive

Model: positive Classifier

# of features: 3,651

F1:0.272,

Precision :0.307,

Rrecall:0.244

Use: HOWNET negative

Model: negative Classifier

# of features: 3,036

F1:0.073,

Precision:0.079,

Rrecall:0.049

Use: NTUSD positive

Model: positive Classifier

# of features: 1,239

F1:0.067,

Precision:0.074,

Rrecall:0.061

Use: NTUSD negative

Model: negative Classifier

# of features: 4,829

F1:0.706,

Precision:0.684,

Recall :0.729

1.3 Appendix III exclusion list and sentence boundary

The following P.O.S tags are exclusion list since they could not have essential meaning for sentimental analysis.

  1. 1.

    ‘Caa’ (tagged as conjunction),

  2. 2.

    ‘D’, ‘DE’, ‘Dfa’ (tagged as adverb),

  3. 3.

    ‘Nh’ (tagged as pronoun),

  4. 4.

    ‘Ndaa’, ‘Ndab’,’Ndc’,’Ndd’ (tagged as time noun),

  5. 5.

    ’Nep’, ‘Neqa’,’Neqb’,’Nes’, ‘Neu’ (tagged as modifier),

  6. 6.

    ‘Nf’, ‘Nfa’, ‘Nfb’, ‘Nfc’, ‘Nfd’, ‘Nfe’, ‘Nfg’, ‘Nfh’, ‘Nfi’, (tagged as quantifier),

  7. 7.

    ‘T’, ‘Ta’, ‘Tb’, ‘Tc’, ‘Td’ (tagged as interjection, auxiliary word),

  8. 8.

    ‘V_2’ (tagged as “有”, i.e. “have” or “has”),

  9. 9.

    ‘SHI’ (tagged as “是”, i.e. “is” or “are”)

To determine the end-of-sentence in opinion, we use following P.O.S tags as sentence boundary:

  1. 1.

    “FW”,

  2. 2.

    “QUESTIONCATEGORY”,

  3. 3.

    “COLONCATEGORY”,

  4. 4.

    “COMMACATEGORY”,

  5. 5.

    “DASHCATEGORY”,

  6. 6.

    “ETCCATEGORY”,

  7. 7.

    “PARENTHESISCATEGORY”,

  8. 8.

    “PAUSECATEGORY”,

  9. 9.

    “PERIODCATEGORY”,

  10. 10.

    “QUESTIONCATEGORY”,

  11. 11.

    “SEMICOLONCATEGORY”,

  12. 12.

    “EXCLANATIONCATEGORY”,

  13. 13.

    “BR”,//HTML mark for end of sentence

  14. 14.

    “SPCHANGECATEGORY”

For more information of Sinica CKIP tagging, please refer to http://ckipsvr.iis.sinica.edu.tw/cat.htm

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Yang, HL., Chao, A.F.Y. Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations. Inf Syst Front 17, 1335–1352 (2015). https://doi.org/10.1007/s10796-014-9498-1

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