• Corpus ID: 17057617

The Impact Of Natural Language Processing-Based Textual Analysis Of Social Media Interactions On Decision Making

  title={The Impact Of Natural Language Processing-Based Textual Analysis Of Social Media Interactions On Decision Making},
  author={Keri Larson and Richard Thomas Watson},
Organizations typically use sentiment analysis-based systems, or even resort to simple manual analysis, to try to derive useful meaning from the public digital “chatter” of their customers. Motivated by the need for a more accurate way to qualitatively mine valuable productand brandoriented consumer-generated text, this paper experimentally tests the ability of an NLP-based analytics approach to extracting knowledge from highly unstructured text. Results indicate that for detecting problems… 

Figures and Tables from this paper

Building a Social Media rating model
The final contribution of this research is building a SM users' rating model for an event using SM data, which will use text-based modeling, data mining techniques, natural process language, machine language, etc. to understand SM content to produce numeric ratings.
An integrated approach to spam classification on Twitter using URL analysis, natural language processing and machine learning techniques
This paper is proposing an application which uses an integrated approach to the spam classification in Twitter which comprises the use of URL analysis, natural language processing and supervised machine learning techniques.
Philippine Computing Journal Dedicated Issue on Natural Language Processing
The challenges of promoting awareness in NLP research, the roles of NLP advocates within educational institutions, as well as the effects of research facilities that support NLPResearch are discussed in this paper.
A performance measurement system to quantify the contribution of social media: new requirements for metrics and methods
Purpose The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data


Opinion Mining and Sentiment Analysis
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Natural Language Processing and Text Mining
This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.
Identifying Sarcasm in Twitter: A Closer Look
This work reports on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author and uses this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm.
Finding high-quality content in social media
This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.
Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification
This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
A Brief Survey of Text Mining
The main analysis tasks preprocessing, classification, clustering, information extraction and visualization are described and a number of successful applications of text mining are discussed.
Text Mining: Natural Language techniques and Text Mining applications
This paper presents two examples of Text Mining tasks, association extraction and prototypical document extraction, along with several related NLP techniques.
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text
Experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality are reported, confirming previous findings linking language and personality, while revealing many new linguistic markers.
Untangling Text Data Mining
Data mining, information access, and corpus-based computational linguistics are defined and the relationship of these to text data mining is discussed, and the intent behind these contrasts is to draw attention to exciting new kinds of problems for computational linguists.
Extracting Relations from Text: From Word Sequences to Dependency Paths
Information extraction systems developed for biological corpora need to be robust to POS or parsing errors, or to give reasonable performance using shallower but more reliable information, such as chunking instead of full parsing.