A Lexical Approach for Opinion Mining in Twitter

Mumtaz, Deebha; Ahuja, Bindiya

doi:10.5815/ijeme.2016.04.03

Cited by 16 publications

(2 citation statements)

References 22 publications

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“…al. [13] formed opinion mining model according to polarity of words and examined effect of negation words. Xia et al [14] proposed an ensemble technique for sentiment classification by integrating different feature sets and classification algorithms to boost the overall performance.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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Abstract-With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

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Section: Related Workmentioning

confidence: 99%

An Ensemble Model using a BabelNet Enriched Document Space for Twitter Sentiment Classification

Sevim¹,

Omurca²,

Ekinci³

2018

IJITCS

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“…The first step in the processing of the user's query (Q) is the Lexical analysis [16], which is typed in English. Each word in the natural language query is separated.…”

Section: A Lexical Analysismentioning

confidence: 99%

SDRED: Smart Data Retrieval Engine for Databases

Ahmad¹,

Zahra²

2018

IJITCS

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Abstract-Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed "Smart Data Retrieval Engine for Databases (SDRED)" provided an environment that allows a nonexpert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user's natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.

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