{ "paper_id": "O17-1016", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T07:59:30.685346Z" }, "title": "TOWARD CONTEXTUAL VALENCE SHIFTERS IN VIETNAMESE REVIEWS", "authors": [ { "first": "Thien", "middle": [], "last": "Khai", "suffix": "", "affiliation": { "laboratory": "", "institution": "Ho Chi Minh City University of Technology VNU-HCM", "location": { "settlement": "Ho Chi Minh City", "country": "Vietnam" } }, "email": "thientk@cse.hcmut.edu.vn" }, { "first": "Tuoi", "middle": [ "Thi" ], "last": "Phan", "suffix": "", "affiliation": { "laboratory": "", "institution": "Ho Chi Minh City University of Technology VNU-HCM", "location": { "settlement": "Ho Chi Minh City", "country": "Vietnam" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Valence shifters are complex linguistic structures that can modify the sentiment orientations of texts. In this paper, the authors concentrate on the study of shifters in Vietnamese texts and a discussion on the distribution of different types of shifters in the hotel reviews is presented. Finally, an approach for extracting the contextual valance shifters is proposed.", "pdf_parse": { "paper_id": "O17-1016", "_pdf_hash": "", "abstract": [ { "text": "Valence shifters are complex linguistic structures that can modify the sentiment orientations of texts. In this paper, the authors concentrate on the study of shifters in Vietnamese texts and a discussion on the distribution of different types of shifters in the hotel reviews is presented. Finally, an approach for extracting the contextual valance shifters is proposed.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Sentiment analysis assumes the task of identifying positive, negative, and neutral thoughts as well as emotional and subjective attitudes of the holder to the target audience, such as products, persons, or topics.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Sentiment analysis is emerging as a research field attracting the scientific community. This includes the construction of emotional lexicons as a basis for other sentiment analysis problems at the document level, sentence level, and aspect level. This work can be done in many ways, with the simplest approach being to manually decide the polarities of sentiment words, and then to have a way of identifying the sentiment for each sentence or for the whole document based on the sentiment values of the words. However, this approach is not appropriate for subjectivity analysis because the sentiment values may be changed in context by the so-called valence shifters [1] . Valence shifters (or \"polarity shifters\", \"sentiment shifter\") are words (or phrases) that can change the sentiment orientations of texts. They are complex linguistic structures that may include explicit negations, contrasts, intensifiers, and diminishers, etc. [2] .", "cite_spans": [ { "start": 667, "end": 670, "text": "[1]", "ref_id": "BIBREF0" }, { "start": 935, "end": 938, "text": "[2]", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "In this paper, the authors present some approaches for contextual valence shifting detection of a Vietnamese sentiment analysis problem. We focus on rule-based methods that may be suitable for the complexity of the linguistic characteristics of Vietnamese. With this paper, the authors describe the research contributions:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "-Identifying many situations that cause valence shifters in Vietnamese texts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "-Proposing an approach to deal with the problem of valence shifters of Vietnamese texts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "To the best of our knowledge, this is the first work to investigate valence shifters in Vietnamese texts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "The authors have organized the rest of this paper as follows: in Section 2 the related work is presented. In Section 3 the valence shifters for Vietnamese are introduced. In Section 4 the authors conclude the paper and discuss possibilities for future work.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "The rise of the \"contextual valence shifters\" phenomenon has made traditional methods become ineffective when used to extract individual terms that indicate prior positive or negative polarity and build a set of emotional words. The fact that the valence of a word/phrase may be modified by one or more words founds the basis of the so-called \"contextual valence shifters\". These shifters were categorized into several types by the authors in [1] , some of them are Negators, Intensifiers, Modals and conditional words, Presuppositional items, and Connectors. [2] , an early publication, deals with valence shifters by pattern rules. The authors created a set of emotional words annotated with their semantic orientation and then used the Mechanical Turk to check the consistence and reliability of the method.", "cite_spans": [ { "start": 443, "end": 446, "text": "[1]", "ref_id": "BIBREF0" }, { "start": 560, "end": 563, "text": "[2]", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "2." }, { "text": "The authors in [3, 4] adopted dependency grammar to develop some syntastic rules for determining the scope of each negator as well as other shifters.", "cite_spans": [ { "start": 15, "end": 18, "text": "[3,", "ref_id": "BIBREF2" }, { "start": 19, "end": 21, "text": "4]", "ref_id": "BIBREF3" } ], "ref_spans": [], "eq_spans": [], "section": "SO-CAL", "sec_num": null }, { "text": "Early sentiment classification work did not pay attention enough to the effect of negators and other shifters, as the authors only used a bag-of-words and n-grams. This meant that two reviews such as \"I like this hotel\" and \"I don't like this hotel\" would be classify to the same emotional category since both contain one sentiment word \"like\", although the first one shows a positive sentiment while the second shows a negative sentiment.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Machine learning, data mining, and deep learning approaches", "sec_num": null }, { "text": "Recently, some remarkable works have adopted machine learning, data mining, or deep learning to successfully consider these problems. In [5] , a semi-automatic approach based on sequence mining was proposed to extract valence shifter patterns that inverted, attenuated, or canceled polarity. This approach covered many valence shifter patterns and reduced the cost of human annotating.", "cite_spans": [ { "start": 137, "end": 140, "text": "[5]", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Machine learning, data mining, and deep learning approaches", "sec_num": null }, { "text": "The authors in [6] used a hybrid approach to deal with the valence shifting problem. At first, a rule-based method was designed to detect shifters. These shifters were then used to train a component classifier of an ensemble method. Along with this, another component classifier was trained on the processed reviews, where the negators were removed and an antonym dictionary (which was built by adopting a weighted loglikelihood ratio algorithm took place of the negators.", "cite_spans": [ { "start": 15, "end": 18, "text": "[6]", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Machine learning, data mining, and deep learning approaches", "sec_num": null }, { "text": "Tien et al. [7] developed VietSentiWordNet, which contains approximately 1,000 lexicons. Hong et al. [8] built Vietnamese sentiment lexicons for product domains. Son et al. [9] built a Vietnamese emotional dictionary with five sub-dictionaries (noun, verb, adjective, adverb, and proposed features). In those, each lexicon is inherently carrying a sentiment polarity that is positive, negative, or neutral. As mentioned in the above section, these polarities may be shifted by the context of the texts. In this section, the authors identify many valence shifter situations in Vietnamese texts and propose some approaches to settle these problems.", "cite_spans": [ { "start": 12, "end": 15, "text": "[7]", "ref_id": "BIBREF6" }, { "start": 101, "end": 104, "text": "[8]", "ref_id": "BIBREF7" }, { "start": 173, "end": 176, "text": "[9]", "ref_id": "BIBREF8" } ], "ref_spans": [], "eq_spans": [], "section": "VALENCE SHIFTERS OF VIETNAMESE TEXTS", "sec_num": "3." }, { "text": "Based on Vietnamese language characteristics, the authors realized five kinds of shifters, and these were Modifier (or Negator), Intensifier, Booster, Diminisher, and Minimizer [10, 11] .", "cite_spans": [ { "start": 177, "end": 181, "text": "[10,", "ref_id": "BIBREF9" }, { "start": 182, "end": 185, "text": "11]", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "VALENCE SHIFTERS OF VIETNAMESE TEXTS", "sec_num": "3." }, { "text": "For the corpus, 14,460 hotel reviews were extracted from mytour.vn. There were 3,829,253 words. The \"AntConc\" software [12] was destined to perform the corpus linguistics research and produced a model of the distribution of different types of valance shifters.", "cite_spans": [ { "start": 119, "end": 123, "text": "[12]", "ref_id": "BIBREF11" } ], "ref_spans": [], "eq_spans": [], "section": "VALENCE SHIFTERS OF VIETNAMESE TEXTS", "sec_num": "3." }, { "text": "A Modifier (or Negator) is the most common kind of valence shifter. For example, in the sentence \"C\u00f4 \u1ea5y kh\u00f4ng th\u00edch c\u00e1i laptop n\u00e0y\" (\"She doesn't like this laptop.\"), the negator \"kh\u00f4ng\" doesn't reverses the valance of the sentiment word \"th\u00edch\" like. We can list some other forms of Modifiers, such as \"kh\u00f4ng\" don't, \"ch\u1ea3\" not, \"ch\u1eb3ng\" no, and \"ch\u1eb3ng bao gi\u1edd\" never, etc. Table I presents the Modifiers that occurred in the corpus very often, the maximum number of occurrences was for the word \"kh\u00f4ng\" with 9,778 from a total of 3,829,253 words in the corpus. ", "cite_spans": [], "ref_spans": [ { "start": 373, "end": 380, "text": "Table I", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "Valence shifting situations in Vietnamese texts Modifier", "sec_num": "3.1" }, { "text": "The sentiment words (or phrases) will decrease their sentiment strength when occurring with a Diminisher or Minimizer. For example, the valence of the sentence \"C\u00f4 \u1ea5y h\u1ecdc kh\u00e1 ch\u0103m ch\u1ec9\" (\"She studies rather hard.\") is lesser than the valence of the sentence \"C\u00f4 \u1ea5y h\u1ecdc ch\u0103m ch\u1ec9\" (\"She studies hard.\"). Some Diminisher/Minimizer words are \"kh\u00e1\" rather, \"h\u01a1i\" quite, and \"ph\u1ea7n n\u00e0o\" somewhat, etc. Table II presents ", "cite_spans": [], "ref_spans": [ { "start": 394, "end": 411, "text": "Table II presents", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "Diminisher and Minimizer", "sec_num": null }, { "text": "The sentiment words (or phrases) will increase their sentiment strength when they occur with an Intensifier or Booster. For example, the valence of the sentence \"C\u00f4 \u1ea5y h\u1ecdc r\u1ea5t ch\u0103m ch\u1ec9\" (\"She studies very hard.\")", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Intensifier and Booster", "sec_num": null }, { "text": "is greater than valence of the sentence \"C\u00f4 \u1ea5y h\u1ecdc ch\u0103m ch\u1ec9\" (\"She studies hard.\"). Some Intensifiers/ Boosters are \"r\u1ea5t\" very, \"c\u1ef1c k\u1ef3\" extremely, and \"v\u00f4 c\u00f9ng\" exceedingly, etc. Table III presents the Intensifiers or Boosters that occur in the corpus, the maximum is reached by the word \"r\u1ea5t\" very with 8,373 occurrences from a total of 3,829,253 words in the corpus. ", "cite_spans": [], "ref_spans": [ { "start": 180, "end": 218, "text": "Table III presents the Intensifiers or", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "Intensifier and Booster", "sec_num": null }, { "text": "Connectors, such as \"m\u1eb7c d\u00f9\" although, \"tuy nhi\u00ean\" however, and \"nh\u01b0ng\" but, etc., can both modify information and work on information elsewhere in the sentence to decrease the force of that information. For example, the valence of the phrase \"C\u00f4 ta th\u00ec xinh nh\u01b0ng kh\u00f4ng t\u1ed1t\" (\"She is pretty but is not kind.\") is equal to the valence of the phrase \"nh\u01b0ng kh\u00f4ng t\u1ed1t\" (\"is not kind\"). Table IV presents the Connectors that occur in the corpus, the maximum is reached by the word \"nh\u01b0ng\" but with 3,728 occurrences from a total of 3,829,253 words in the corpus. ", "cite_spans": [], "ref_spans": [ { "start": 384, "end": 392, "text": "Table IV", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "Connectors", "sec_num": null }, { "text": "The valance of a cause-effect sentence is the valance of the effect clause. For example, in the sentence \"V\u00ec c\u00f4 \u1ea5y ch\u0103m ch\u1ec9 n\u00ean c\u00f4 \u1ea5y thi \u0111\u1eadu.\" (\"She passed the exam because she studied hard.\"), the valence of this sentence is equal the valence of the clause \"she studied hard\". Some cause-effect words are \"v\u00ec...n\u00ean\" because \u2026 that, \"v\u00ec\u2026m\u00e0\" because \u2026 that, and \"b\u1edfi v\u00ec\u2026m\u00e0\" because \u2026 that, etc.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Cause-Effect sentences", "sec_num": null }, { "text": "Conditional sentences often do not contain emotion. We cannot determine the valance of these sentences because of the \"if-then\" statement. For example, in the sentence \"N\u1ebfu laptop \u0111\u00f3 r\u1ebb th\u00ec t\u00f4i s\u1ebd mua m\u1ed9t c\u00e1i\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conditional sentences", "sec_num": null }, { "text": "(\"If that laptop is cheap then I will buy it.\"), we do not know whether the laptop is \"cheap\" or not. Some conditional words are \"n\u1ebfu \u2026 th\u00ec\" if\u2026then, \"h\u1ec5 \u2026 th\u00ec\" if\u2026then, and \"gi\u1ea3 s\u1eed \u2026 th\u00ec\" if\u2026then, etc.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conditional sentences", "sec_num": null }, { "text": "Questions also do not contain emotion because we cannot determine the polarities of the words in the texts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Questions", "sec_num": null }, { "text": "For example, in the question \"Laptop \u0111\u00f3 c\u00f3 ok kh\u00f4ng?\" (\"Is that laptop ok?\"), we do not know whether the laptop is \"ok\" or not.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Questions", "sec_num": null }, { "text": "We can capture the above situations by using some rule-based methods built on the presence of many predefined patterns. In [13] , we proposed an approach for mining features and opinion words based on an upgraded double propagation algorithm [14] , some regular expression rules and ontologies. However, as mentioned in [10, 11] , there are several exceptions based on the linguistics characteristics of Vietnamese.", "cite_spans": [ { "start": 123, "end": 127, "text": "[13]", "ref_id": null }, { "start": 242, "end": 246, "text": "[14]", "ref_id": "BIBREF13" }, { "start": 320, "end": 324, "text": "[10,", "ref_id": "BIBREF9" }, { "start": 325, "end": 328, "text": "11]", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "For example, the kind of adjective that goes along with negators or the position between the shifters and adjectives (or verbs), etc.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "Moreover, we also pay attention to some special words/phases that can modify the valence of Vietnamese texts, as follows:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "\u2022 Example 1: \"Kh\u00e1ch s\u1ea1n \u1ea5y \u0111\u00e3 t\u1eebng \u0111\u01b0\u1ee3c \u01b0a chu\u1ed9ng\" (\"This hotel used to be popular.\"). In this review, the word \"\u01b0a chu\u1ed9ng\" popular shows a positive emotion, but the whole sentence shows a negative emotion because of the word \"\u0111\u00e3 t\u1eebng\" used to be.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "\u2022 Example 2: \"Hotel \u1ea5y m\u1edbi nh\u00ecn c\u00f3 v\u1ebb t\u1ed1t\" (\"At first glance, this hotel seems to be good.\"). In this example, the word \"t\u1ed1t\" good shows a positive emotion but the whole sentence does not show a positive emotion because of the phrase \"m\u1edbi nh\u00ecn\" at first glance.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "After capturing the words/phrases that are the valence shifters in texts, we can identify their sentiment scores in the same way as the authors in [10, 11] have done.", "cite_spans": [ { "start": 147, "end": 151, "text": "[10,", "ref_id": "BIBREF9" }, { "start": 152, "end": 155, "text": "11]", "ref_id": "BIBREF10" } ], "ref_spans": [], "eq_spans": [], "section": "Approach to deal with contextual valance shifters in Vietnamese texts", "sec_num": "3.2" }, { "text": "This paper presented valence shifters in the Vietnamese language and proposed some approaches to deal with this problem. In the paper, a discussion of the distribution of different types of shifters was conducted using hotel reviews. Based on this, and via investigating the linguistic characteristics of Vietnamese, the authors intend to build effective rules for extracting reliable shifters. In future work, the proposed rules will be actualized and machine learning or deep learning methods will be adopted. This will help the system become more flexible and robust.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusions", "sec_num": "4." } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Contextual valence shifters. Computing attitude and affect in text: Theory and applications", "authors": [ { "first": "L", "middle": [], "last": "Polanyi", "suffix": "" }, { "first": "A", "middle": [], "last": "Zaenen", "suffix": "" } ], "year": 2006, "venue": "", "volume": "", "issue": "", "pages": "1--10", "other_ids": {}, "num": null, "urls": [], "raw_text": "L. 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Comput Linguist 37(1): 9-27.", "links": null } }, "ref_entries": { "FIGREF0": { "type_str": "figure", "text": "the Diminishers or Minimizers that occurred in the corpus, the maximum is reached by the word \"kh\u00e1\" rather with 5,977 occurrences from a total of 3,829,253 words in the corpus.", "num": null, "uris": null }, "TABREF0": { "content": "", "type_str": "table", "html": null, "text": "The 2017 Conference on Computational Linguistics and Speech Processing ROCLING 2017, pp. 152-159 \uf0d3 The Association for Computational Linguistics and Chinese Language Processing", "num": null }, "TABREF1": { "content": "
ShiftersOccurrences in the corpus
kh\u00f4ng
don't/doesn't
ch\u1eb3ng no
ch\u1ea3 not
", "type_str": "table", "html": null, "text": "STATISTICS OVER THE MODIFIER FREQUENCY IN THE HOTEL REVIEWS CORPUS.", "num": null }, "TABREF2": { "content": "
REVIEWS CORPUS.
ShiftersOccurrences in the corpus
kh\u00e1 rather
h\u01a1i quite
ph\u1ea7n n\u00e0o
somewhat
", "type_str": "table", "html": null, "text": "STATISTICS OVER THE DIMINISHER OR MINIMIZER FREQUENCY IN THE HOTEL", "num": null }, "TABREF3": { "content": "
REVIEWS CORPUS.
ShiftersOccurrences in the corpus
r\u1ea5t very
c\u1ef1c k\u1ef3 extremely
v\u00f4 c\u00f9ng
exceedingly
", "type_str": "table", "html": null, "text": "STATISTICS OVER THE INTENSIFIER AND BOOSTER FREQUENCY IN THE HOTEL", "num": null }, "TABREF4": { "content": "
ShiftersOccurrences in the corpus
m\u1eb7c d\u00f9 although
tuy nhi\u00ean
however
nh\u01b0ng but
", "type_str": "table", "html": null, "text": "STATISTICS OVER THE CONNECTOR FREQUENCY IN THE HOTEL REVIEWSCORPUS.", "num": null } } } }