Microblog sentiment analysis with weak dependency connections

Zou, Xiaomei; Yang, Jing; Zhang, Jianpei; Han, Hao

doi:10.1016/j.knosys.2017.11.035

Cited by 32 publications

(19 citation statements)

References 29 publications

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“…Subsequently, textual analysis of the negative, neutral, and positive reviews was conducted using the qualitative analysis software Nvivo Pro 12 [10,69,70], which identifies key factors for the management of environmental issues by Swiss hotels using the presented process as a methodological support in the research conducted by Saura et al [3,49,71,72].…”

Section: Methodsmentioning

confidence: 99%

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

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Abstract:The main aim of this study was to identify the key indicators related to environmental management and sustainability of hotels as perceived by travelers during their trips. The methodology used was a sentiment analysis with an algorithm developed in Python trained with data mining and machine learning, with the MonkeyLearn library in the hotel industry sector under the eWOM model (e-Word of Mouth). The results with negative, positive and neutral feelings were submitted to a textual analysis with the qualitative analysis software Nvivo Pro 12. The sample consisted of the 25 best hotels in Switzerland according to Traveler's Choice from TripAdvisor ranking 2018 that draws from more than 500,000 reviews. For data extraction, we connected to the TripAdvisor API, obtaining a sample of n = 8331 reviews of the hotels that made up the ranking. The results of the study highlight the key factors related to environmental management detected by travelers during their stay in hotels and can be meaningfully used by managers or hotel managers to improve their services and enhance the value provided by their policies of sustainability and respect for the environment. The limitations of the present study relate to the size of the sample and the number of hotels included in the present analysis.

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Section: Methodsmentioning

confidence: 99%

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

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“…The various benchmark datasets used in the past decade were WePS-3, 27 SemEval, 30,52,54,55,73,75,76,85 tweets prepared by Stanford University, 34,45,46,75 SNAP, 40 Sanders Twitter Sentiment Corpus (denoted as Sanders), 44,55,75,79 2008 Presidential Debate Corpus, 44,75,79 Sentiment140, 51 RepLab 2012, 53 RepLab 2013, 53 STS-manual, 55 Gold Standard personality labeled Twitter dataset, 59 Cleveland Heart Disease data, 69 STS-Gold, 73 FIGURE 6 Distribution of papers in accordance to the digital libraries (expressed in percentages) Many reported researches were carried on the tweets fetched directly from Twitter using its API. The tweets were from a variety of domains, topics and time period (referred as topic specific/topic oriented tweets).…”

Section: • Widely Used Datasets and Domains In Which The Studies For mentioning

confidence: 99%

“…The tweets were from a variety of domains, topics and time period (referred as topic specific/topic oriented tweets). These prominently included tweets from or about elite personalities like actors; singers; sportsperson; comedians; politicians, authors, idols; entertainers, 28,34,37,40,54,55,67,70,73,75,79 etc, news and commemoratives, 17,30,48,58,59,64,67,79 health and fitness, 31,56,57,69,74,75,78,79 stock market exchanges, 29,34,63,82 companies like AT&T; Amazon;…”

Section: • Widely Used Datasets and Domains In Which The Studies For mentioning

confidence: 99%

“…Apple; Google; Microsoft, 27,29,34,44,55,73,75 consumer products like kindle, smart-phones, etc, 51,55,56,73,84 natural calamities, energy and environmental related, 36,46,77 cyber hatred, 47,62 entertainment 29,49,53,84 which includes tweets about music and movies, automotive or vehicles, 49,53 banking, 53 government or public campaign or public administration, 33,37,39,57,72,79 education or universities, 43,53,57,67 science or technology, 40,49,57 politics, 17,39,40,49,61,67,72,79,80,84 sports, 40,49,60 daily deals and discount, 56 trade or commercial services or business or financial...…”

Section: • Widely Used Datasets and Domains In Which The Studies For mentioning

confidence: 99%

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Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Kumar

Jaiswal

2019

Concurrency and Computation

133

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Sentiment detection and classification is the latest fad for social analytics on Web. With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. Soft computing techniques have been used to handle this fuzziness in practical applications. This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain. KEYWORDS machine learning, review, sentiment analysis, soft computing, Twitter INTRODUCTIONThe incessantly evolving dynamics of the Web in terms of the volume, velocity and variety of opinion-rich information accessible online, has made research in the domain of Sentiment Analysis (SA) a trend for many practical applications which facilitate decision support and deliver targeted information to domain analysts. Interestingly, the buzzing term ''big data'' which is estimated to be 90% unstructured 1 further makes it crucial to tap and analyze information using contemporary tools. Text mining models define the process to transform and substitute this unstructured data into a structured one for knowledge discovery. Use of classification algorithms to intelligently mine text has been studied extensively across literature. 2,3 SA, established as a typical text classification task, 4 is defined as the computational study of people's opinions, attitudes and emotions towards an entity. 5,6 It offers a technology-based solution to understand people's reactions, views and opinion polarities (positive, negative or neutral) in textual content available over social media sources.Research studies and practical applications in the field of SA have escalated in the past decade with the transformation and expansion of Web from passive provider of content to an active socially-aware distributor of collective intelligence. This new collaborative Web (called Web 2.0), 7 extended by Web-based technologies like comments, blogs and wikis, social media portals like Twitter or Facebook, that allow to build social networks based on professional relationship, i...

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“…Lee [34] identified individual user sentiments embedded in the messages, task-oriented content, and proactiveness to analyze collective sentiments, which can affect collective cocreation thinking, especially for the innovation process of cocreation communities. Zou [35] utilized community detection methods to investigate how to exploit weak dependency connections in communities as an aspect of social contexts for microblog sentiment analysis, including sentiment consistency and emotional contagion. In our paper, we detect a resonant sentimental community and identify the most similar users with concordant sentiments for personalized recommendations.…”

Section: Community Detectionmentioning

confidence: 99%

Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users' implicit interests. Sentiments on topics imply users' sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.

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Microblog sentiment analysis with weak dependency connections

Cited by 32 publications

References 29 publications

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

Personalized Recommendations Based on Sentimental Interest Community Detection

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