Construction of a sentimental word dictionary

Dragut, Eduard C.; Yu, Clement; Sistla, Prasad; Meng, Weiyi

doi:10.1145/1871437.1871723

Cited by 75 publications

(22 citation statements)

References 12 publications

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“…By using a deduction approach, the resulting sentimental dictionary contains approximately 50% more words than a given sentimental word dictionary. [11]…”

Section: Construction Of Dictionary Of Sentimentsmentioning

confidence: 99%

Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference

Bali¹,

Agarwal²,

Poddar³

et al. 2016

IJCA

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In today"s era, which is symbolic of "information overload", we are living in a time where knowledge needs to be mined from information which is available in abundance, one such instance being mining sentiments from user textual reviews which plays a decisive role in consumer"s decision making processes. Today"s customers are intelligent enough and will cross-check before purchasing any product and the best place for that are online reviews which are ubiquitous. In today"s competitive market, analyzing user needs and sentiments is of uttermost importance as one bad review can make or break the profits of a brand since Word of Mouth plays a significant role in building a customer base for a brand or product.

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“…By using a deduction approach, the resulting sentimental dictionary contains approximately 50% more words than a given sentimental word dictionary. [11]…”

Section: Construction Of Dictionary Of Sentimentsmentioning

confidence: 99%

Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference

Bali¹,

Agarwal²,

Poddar³

et al. 2016

IJCA

View full text Add to dashboard Cite

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“…In [26], authors use linear programming to update the polarity of words based on specified hard or soft constraints. Another application of linear programming appears in [27] to learn a sentiment lexicon which is not only domain specific but also aspect-dependent. Another recent work expands a given dictionary of words with known polarities by first producing a new set of synonyms with polarities and using these to further deduce the polarities of other words [28].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Gezi̇ci

Yanıkoğlu

Tapucu

et al. 2015

Advances in Social Media Analysis

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Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We apply a simple method to adapt a general purpose polarity lexicon to a specific domain [1]. Subsequently, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step for estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. We use a subset of hotel reviews from the TripAdvisor database [2] to evaluate the effect of sentence-level features on sentiment classification. Then, we measure the performance of our sentiment analysis engine using the domain-adapted lexicon on a large subset of the TripAdvisor database.

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“…Authors like Jindal and Liu [12] describe this analytical method. Using a relatively small number of words as comparative adverbial adjectives mai mult, mai puţin, uşoare 3 , superlative adjectives and adverbs mai, cel puţin, cele mai bune 4 , additional clauses favoare, mare, preferă, decât, superioară, inferior, numărul unu, împotriva 5 , we can cover 98 % of the comparative opinions.…”

Section: Comparative Sentiment Analysismentioning

confidence: 99%

Detecting Emotions in Comments on Forums

Gîfu

Cioca

2014

INT J COMPUT COMMUN

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The paper presents one of the most important issues in Natural Language Processing (NLP), emotion identification and classification to implement a computational technology based on existing resources, open-source or freely available for research purposes. Furthermore, we are interested to use it for establishing Gold standards in sentiment analysis area, such as SentiWordNet. In this sense, we propose to recognize and classify the emotions (sentiments) of the public consumer from the written texts which appeared on the various Forums. We analyse the writing style which refers to how consumers construct sentences together when they write comments to indicate their passion about an entity (persons, brand, location, etc.). We present in this paper a method for integrating Romanian lexical resources from emotional perspective, in developing, which can be used in sentiment analysis. This study is intend to help direct beneficiaries (public consumer, marketing managers, PR firms, politicians, investors), but, also, specialists and researchers in the field of natural language processing, linguists, psychologists, sociologists, economists, etc.

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Construction of a sentimental word dictionary

Cited by 75 publications

References 12 publications

Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference

Consumer’s Sentiment Analysis of Popular Phone Brands and Operating System Preference

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Detecting Emotions in Comments on Forums

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