Deriving Business Value From Online Data Sources Using Natural Language Processing Techniques

Author:

Camilleri Stephen1

Affiliation:

1. University of Malta, Malta

Abstract

The wealth of information produced over the internet empowers businesses to become data-driven organizations, increasing their ability to predict consumer behavior, take more informed strategic decisions, and remain competitive on the market. However, past research did not identify which online data sources companies should choose to achieve such an objective. This chapter aims to analyse how online news articles, social media messages, and user reviews can be exploited by businesses using natural language processing (NLP) techniques to build business intelligence. NLP techniques assist computers to understand and derive a valuable meaning from human (natural) languages. Following a brief introduction to NLP and a description of how these three text streams differ from each other, the chapter discusses six main factors that can assist businesses in choosing one data source from another. The chapter concludes with future directions towards improving business applications involving NLP techniques.

Publisher

IGI Global

Reference58 articles.

1. EvenTweet

2. Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments

3. Alfonseca, E., Pighin, D., & Garrido, G. (2013). HEADY: News headline abstraction through event pattern clustering. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 1243-1253). Association for Computational Linguistics.

4. Sentiment analysis and topic detection of Spanish Tweets: A comparative study of NLP techniques.;A. F.Anta;Procesamiento de Lenguaje Natural,2013

5. Arendse, B. (2016). A thorough comparison of NLP tools for requirements quality improvement (Master’s thesis). Utrecht University.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3