Text analytics in industry: Challenges, desiderata and trends

Ittoo, Ashwin; Nguyen, Le-Minh; Bosch, Antal van den

doi:10.1016/j.compind.2015.12.001

Cited by 79 publications

(44 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Text mining can improve the user experience (He, 2013) and improve the understanding of customer behavior and patterns (Chau & Xu, 2012). One famous example of text analytics is Netflix, which exploits huge volumes of data collected from members to improve the quality of the movies (Ittoo et al, 2016). In addition, opinion mining and sentiment analysis combines natural language processing (NLP), information retrieval (IR), structured and unstructured text analysis and computational linguistics to identify the text sentiment (Ravi & Ravi, 2015;Vinodhini & Chandrasekaran, 2016).…”

Section: Literature Review Big Data and Text Analyticsmentioning

confidence: 99%

“…Numerous e-commerce companies use highly scalable ecommerce platforms and social media platforms to expand the volume of unstructured data as digital text. Managers can then base their decisions on the information extracted from these sources (Ittoo, Nguyen, & van den Bosch, 2016). Web data posted on social media website express posters' opinions and their feelings and understand customers, competitors, products, business environment, impact of technologies, and strategic stakeholders such as alliance and suppliers (Xiang et al, 2015).…”

Section: Literature Review Big Data and Text Analyticsmentioning

confidence: 99%

See 1 more Smart Citation

Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Ting

Chen

et al. 2017

CMR

View full text Add to dashboard Cite

This study combines programming and data mining to analyze consumer reviews extracted from Yelp.com to deconstruct the hotel guest experience and examine its association with satisfaction ratings. The findings show many important factors in customer reviews that carry varying weights and find the meaningful semantic compositions inside the customer reviews. More importantly, our approach makes it possible to use big data analytics to find different perspectives on variables that might not have been studied in the hospitality literature.

show abstract

Section: Literature Review Big Data and Text Analyticsmentioning

confidence: 99%

Section: Literature Review Big Data and Text Analyticsmentioning

confidence: 99%

Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Ting

Chen

et al. 2017

CMR

View full text Add to dashboard Cite

show abstract

“…Some of the challenges are listed below [7]: I. Heterogeneity of data is one of the biggest challenges for text miners to deal.…”

Section: Section (E): Conclusion and Future Workmentioning

confidence: 99%

Importance of Text Data Preprocessing & Implementation in RapidMiner

Kalra¹,

Aggarwal²

2018

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Data preparation is an important phase before applying any machine learning algorithms. Same with the text data before applying any machine learning algorithm on text data, it requires data preparation. The data preparation is done by data preprocessing. The preprocessing of text means cleaning of noise such as: cleaning of stop words, punctuation, terms which doesn't carry much weightage in context to the text, etc. In this paper, we describe in detail how to prepare data for machine learning algorithms using RapidMiner tool. This preprocessing is followed by conversion of bag of words into term vector model and describe about the various algorithms which can be applied in RapidMiner for data analysis and predictive modeling. We also discussed about the challenges and applications of text mining in recent days Index Terms-RapidMiner, Preprocessing of text, TF-IDF, Term Vector Model.

show abstract

“…The focus of this paper is text-based analytics, which includes natural language processing and text mining (Ittoo, Nguyen and van den Bosch, 2016). Fan & Gordon (2014) presented social media analytics as a three-stage process: (1) capture, (2) understand, and (3) present.…”

Section: Literature Reviewmentioning

confidence: 99%

Competitive analysis of online reviews using exploratory text mining

Amadio¹,

Procaccino²

2016

Tour. hosp. manag.

View full text Add to dashboard Cite

Purpose -This paper explores the usefulness of analyzing text-based online reviews using text mining tools and visual analytics for SWOT Analysis, as applied to the hotel industry. These results can be used to develop competitive actions. Design -The text mining/visualization tool, ReviewMap, was used to transform an archive of reviews spanning multiple suppliers into a hierarchy of data of increasing dimensionality. Visual summaries at each level were integrated to propagate selections at one level throughout the rest of the hierarchy. These visual summaries identify features required for competition at a given level and features that currently discriminate amongst competitors. Methodology -The approach was exploratory, the objective of which was to determine if useable competitive intelligence could be found in a typical collection of online reviews for a set of competing hotels. A publically available collection of reviews was subjected to a set of text mining procedures and visual analyses in order to summarize the features and opinions expressed. Originality -Prior analyses of online reviews relied solely upon numeric "star" ratings. This study utilized text mining to uncover information within the written comments and applied the information in a SWOT Analysis of three competing hotels. Findings -In the set of reviews used in this paper, a common measure of analytical power almost doubled when text mining summaries of the written comments were used in combination with numeric ratings. Visual analytics revealed the dominant features for each hotel, the features required of all hotels competing at a given level, and the features that define specific positions within the competitive landscape. This analysis of strengths, weaknesses, opportunities and threats revealed several promising competitive actions for the hotels in the study.

show abstract

Text analytics in industry: Challenges, desiderata and trends

Cited by 79 publications

References 38 publications

Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Using Big Data and Text Analytics to Understand How Customer Experiences Posted on Yelp.com Impact the Hospitality Industry

Importance of Text Data Preprocessing & Implementation in RapidMiner

Competitive analysis of online reviews using exploratory text mining

Contact Info

Product

Resources

About