Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Kybernetes ◽  
2019 ◽  
Vol 49 (11) ◽  
pp. 2633-2649
Author(s):  
Duen-Ren Liu ◽  
Yun-Cheng Chou ◽  
Ciao-Ting Jian
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online News ◽  
Content Type ◽  
Online Evaluation ◽  
News Websites ◽  
News Website ◽  
Recommendation Diversity ◽  
Cross Type ◽  
News Online

Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

Online news recommendations based on topic modeling and online interest adjustment

2019 ◽  
Vol 119 (8) ◽  
pp. 1802-1818
Author(s):  
Duen-Ren Liu ◽  
Yu-Shan Liao ◽  
Jun-Yi Lu
Keyword(s):  
Topic Modeling ◽  
Recommendation System ◽  
Online News ◽  
User Preferences ◽  
Online Media ◽  
Content Type ◽  
Online Evaluation ◽  
Important Trend ◽  
News Recommendation ◽  
Click Through Rate

Purpose Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms. Design/methodology/approach A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing. Findings This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation. Originality/value The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

CID: Categorical Influencer Detection on microtext-based social media

2020 ◽  
Vol 44 (5) ◽  
pp. 1027-1055
Author(s):  
Thanh-Tho Quan ◽  
Duc-Trung Mai ◽  
Thanh-Duy Tran
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Learning Approaches ◽  
Content Type ◽  
Modeling Process ◽  
Variational Autoencoder ◽  
Media Channels

PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

Analyzing the startup ecosystem of India: a Twitter analytics perspective

2019 ◽  
Vol 17 (2) ◽  
pp. 262-281 ◽  
Author(s):  
Shiwangi Singh ◽  
Akshay Chauhan ◽  
Sanjay Dhir
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Relevant Information ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Content Type ◽  
Twitter Analytics ◽  
Bayes Algorithm

Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

Tourists’ shifting perceptions of UNESCO heritage sites: lessons from Jeju Island-South Korea

2019 ◽  
Vol 74 (1) ◽  
pp. 20-29 ◽  
Author(s):  
Kun Kim ◽  
Ounjoung Park ◽  
Jacob Barr ◽  
Haejung Yun
Keyword(s):  
Topic Modeling ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Natural World ◽  
Tourism Industry ◽  
Jeju Island ◽  
Web Crawler ◽  
Content Type ◽  
Heritage Sites

Purpose The purpose of this research is to analyze the shifting perceptions of international tourists to Jeju Island and provide practical lessons to the tourism industry. Specifically, in regard to three United Nations Educational, Scientific and Cultural Organization (UNESCO) natural World Heritage sites in Jeju, this research measures the most salient topics mentioned by tourists to inform a more accurate perception of the island’s most valuable natural assets as reported by tourism experiences. Design/methodology/approach This study used a Web crawler to gather over 1,500 English language reviews from international tourists from a famous travel information website. The collected data were then preprocessed for stemming and lemmatization. After this, the processed text data were analyzed through a latent Dirichlet allocation (LDA)-based topic modeling approach to identify the most prominent clusters of ideas mentioned and represent them visually through graphs, tables and charts. Findings The findings from this research suggest that there are ten identifiable topics. Topics focusing on “adventure,” “summits” and “winter” showed noticeable increases, whereas topics focusing on “sunrise peak” and “UNESCO” have decreased over time. There is a trend for international tourists to be ever more conscious of the adventurous and rugged aspects of Jeju, and the novelty of mentioning UNESCO status seems to have worn off. Furthermore, there is the proclivity for tourists to mention “worth” and “enjoy” more as time goes on. Originality/value This study applies LDA-based topic modeling and LDAvis using user-generated online reviews with time-series analyses. Consequently, it provides unique insights into the changing perceptions of ecotourism on Jeju today, as well as contribution to smart tourism fields.

Corporate disclosure via social media: a data science approach

2020 ◽  
Vol 44 (1) ◽  
pp. 278-298
Author(s):  
Marian H. Amin ◽  
Ehab K.A. Mohamed ◽  
Ahmed Elragal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Topic Modeling ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Gender Diversity ◽  
Financial Disclosure ◽  
Extensive Literature ◽  
Content Type ◽  
Board Characteristics

Purpose The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information. Design/methodology/approach This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation topic modeling to identify financial disclosure tweets. Panel, Logistic and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics. Findings Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity and board tenure. Originality/value The extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive vs non-executive directors relating to disclosure decisions.

scholarly journals Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM

2021 ◽  
Vol 5 (1) ◽  
pp. 24 ◽  
Author(s):  
Chairullah Naury ◽  
Dhomas Hatta Fudholi ◽  
Ahmad Fathan Hidayatullah
Keyword(s):  
Mass Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Short Term Memory ◽  
Online News ◽  
Distance Map ◽  
Analysis Process ◽  
Modeling Process ◽  
News Headlines

The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.

Study of the Automatic Evaluation of Website Quality from Customer Insight

2015 ◽  
pp. 178-197 ◽  
Author(s):  
Molood Arman ◽  
Hassan Hajipoor ◽  
Babak Sohrabi
Keyword(s):  
Online News ◽  
Customer Relationship ◽  
Automatic Evaluation ◽  
Technical Aspects ◽  
Customer Insight ◽  
News Websites ◽  
News Website ◽  
Relationship Of ◽  
The Relationship

Effectiveness of websites is largely dependent on the quality of the website. The biggest share of the quality`s new concept is that the technical aspects of products and services combines with customers usage and understanding. Therefore websites evaluation based on the maximum usage and perception of the customers is considered an important issue to announce to the related organizations the success of website from customers' views. This customer relationship need a kind of management that first step of that for future decision needs knowledge about the websites features, customer insight and the position of websites among the competitors. One of the available media is the online news websites which their success is highly dependent on the relationship of their users. In this article achieving the information of websites is automatic and without the intervention of human so that the instant evaluation could be possible and used method is TOPSIS combined with information entropy to rank 791 news website which have most visitors of the Iranian users based on Alexa ranking report.

Communities of co-commenting in the Russian LiveJournal and their topical coherence

2016 ◽  
Vol 26 (3) ◽  
pp. 710-732 ◽  
Author(s):  
Olessia Koltsova ◽  
Sergei Koltcov ◽  
Sergey Nikolenko
Keyword(s):  
Social Studies ◽  
Topic Modeling ◽  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Opinion Leaders ◽  
Reliability Of Results ◽  
Policy Makers ◽  
Content Type ◽  
The Social ◽  
Practical Implications

Purpose – The paper addresses the problem of what drives the formation of latent discussion communities, if any, in the blogosphere: topical composition of posts or their authorship? The purpose of this paper is to contribute to the knowledge about structure of co-commenting. Design/methodology/approach – The research is based on a dataset of 17,386 full text posts written by top 2,000 LiveJournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting, where posts are vertices. The Louvain algorithm is used to detect communities of co-commenting. Cosine similarity and topic modeling based on latent Dirichlet allocation are applied to study topical coherence within these communities. Findings – Bloggers unite into moderately manifest communities by commenting roughly the same sets of posts. The graph of co-commenting is sparse and connected by a minority of active non-top commenters. Communities are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics. Research limitations/implications – The research has to be replicated on other datasets with more thorough hand coding to ensure the reliability of results and to reveal average proportions of topic-centered communities. Practical implications – Knowledge about factors around which co-commenting communities emerge, in particular clustered opinion leaders that often attract such communities, can be used by policy makers in marketing and/or political campaigning when individual leadership is not enough or not applicable. Originality/value – The research contributes to the social studies of online communities. It is the first study of communities based on co-commenting that combines examination of the content of commented posts and their topics.

Hypertext in online news stories

2014 ◽  
Vol 21 (2) ◽  
pp. 163-178
Author(s):  
Luuk Lagerwerf ◽  
Daniël Verheij
Keyword(s):  
Perceived Control ◽  
Personal Characteristics ◽  
Online News ◽  
Plain Text ◽  
News Stories ◽  
Additional Information ◽  
News Websites ◽  
News Website ◽  
Dependent Variables

News websites struggle tailoring news stories to divergent needs of online news users. We examined a way to bridge these needs by representing sources in hypertext. News items were designed to be short and concise, with hyperlinks citing sources. Readers could either ignore hyperlinks or explore additional information from the hyperlinked sources. We expected that appreciation for these news stories would be moderated by personal characteristics, namely hypertext comfort and desirability of control. In a 2 (hyperlink presence) x 2 (directness of speech) experiment, two news stories were manipulated for a Dutch national news website (NOS.nl). For each story, four variants were developed: Text containing hyperlinks, plain text only, citing the sources directly, citing in the words of the journalist. Dependent variables were perceived control, appreciation, and absorption in the story. Results showed that news stories with hyperlinked sources affected perceived control positively, especially for those with a high desirability of control. Directness of speech did not have any effects. The relation between hypertext and appreciation was mediated by perceived control.

A text mining-based review of the literature on dynamic capabilities perspective in information systems research

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mojtaba Talafidaryani
Keyword(s):  
Information Systems ◽  
Topic Modeling ◽  
Dynamic Capabilities ◽  
Latent Dirichlet Allocation ◽  
Content Type ◽  
Systems Research ◽  
Information Systems Research ◽  
Holistic Understanding ◽  
Prior Literature ◽  
Dynamic Capabilities Perspective

Purpose While the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research. Design/methodology/approach Using latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings. Findings In accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years. Originality/value The current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

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