RoboTech: 2014

ABSTRACT:

This research is intended to give detailed overview of basic concept of two text mining techniques namely information retrieval and information extraction.

This paper has provided a comparison table of both these techniques on the basis of characteristic and their relationship to each other.

We have also underlined many interesting research challenges which will benefit in managing the extracted valuable information.

Later this research has also been motivated to highlight the main role of information extraction in terms of retrieval context in future to be played.

KEYWORD:

Information retrieval, Information extraction, Text mining

CITE:

Ahmad, P.H., & Dang, S. (2014). A Comparative Study on Text mining Techniques.

PUBLINK:

http://www.gjar.org/articles/A-Comparative-Study-on-Text-mining-Techniques

DOCLINK:

https://www.ijsr.net/archive/v3i12/U1VCMTQ4NjU=.pdf

RGTLINK:

https://www.researchgate.net/publication/270704468_A_Comparative_Study_on_Text_mining_Techniques

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage.

In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood.

Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com.

By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na\"{\i}ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

approach, affective lexicon building, emotion scores, unsupervised settings, sentiment analysis

https://aclanthology.org/P14-2070/

https://www.semanticscholar.org/paper/Depeche-Mood%3A-a-Lexicon-for-Emotion-Analysis-from-Staiano-Guerini/492cefde909957fd8b0f77f75e51b066567671b6

https://aclanthology.org/P14-2070.pdf

https://app.razzi.my/findgref?gid=1_P_NOkeQQ0T5lUUS6qwW-qRUb9MFp1FI