Improvement of Email And Twitter Classification Accuracy Based On Preprocessing Bayes Naive Classifier Optimization In Integrated Digital Assistant

Erianda, Aldo; Rahmayuni, Indri

doi:10.30630/joiv.1.2.21

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Purwarianti, Andhika, Wicaksono, Afif, and Ferdian [8] proposed a processing toolkit for natural language in Indonesian that contains many natural language processing modules. A. Erianda and I. Rahmayuni [27] bring off research on how to improve the classification of Twitter and email data using naive bayes. Their research concluded that word noise can reduce the accuracy of the classification system.…”

Section: Introductionmentioning

confidence: 99%

Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis

Islami

Barakbah

Harsono

2021

JOIV : Int. J. Inform. Visualization

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A company maintains and improves its quality services by paying attention to reviews and complaints from users. The complaints from users are commonly written using human natural language expression so that their messages are computationally difficult to extract and proceed. To overcome this difficulty, in this study, we presented a new system for issues feature extraction from users’ reviews and complaints from social media data. This system consists of four main functions: (1) Data Crawling and Preprocessing, (2) Categorization Knowledge Modelling, (3) Rule-based Sentiment Analysis, and (4) Application Environment. Data Crawling and Preprocessing provides data acquisition from users’ tweets on social media, crawls the data and applies the data preprocessing. Categorization Knowledge Modelling provides text mining of textual data, vector space transformation to create knowledge metadata, context recognition of keyword queries to the knowledge metadata, and similarity measurement for categorization. In the Rule-based Sentiment Analysis, we developed our own rules of computatioal linguistics to measure polarity of sentiment. Application Environment consists of 3 layers: database management, back-end services and front-end services. For applicability of our proposed system, we conducted two kinds of experimental study: (1) categorization performance, and (2) sentiment analysis performance. For categorization performance, we used 8743 tweet data and performed 82% of accuracy. For categorization performance, we made experiments on 217 tweet data and performed 92% of accuracy.

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Section: Introductionmentioning

confidence: 99%

Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis

Islami

Barakbah

Harsono

2021

JOIV : Int. J. Inform. Visualization

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“…In terms of Arabic sentiment analysis on Twitter [45] or recommending not only items of interest to the user, but also the conditions are enhancing user experiences with those items [47] are also investigated. Other researchers used naïve Bayes as a text classification algorithms to improve the accuracy of email and Twitter classification [48]. Twitter more than other social networks favors the sentiment analysis due to its "public" nature and offers some APIs 1 that allow displaying the tweets of users, to get the followers and following, to search for tweets by content, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysing and Visualizing Tweets for U.S. President Popularity

Luca

Fallucchi

Giuliano

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

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Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App

Pilliang

Akbar

Firmansyah

2022

2022 3rd International Conference on Electrical Engineering and Informatics (ICon EEI)

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Improvement of Email And Twitter Classification Accuracy Based On Preprocessing Bayes Naive Classifier Optimization In Integrated Digital Assistant

Abstract: Abstract-This research focuses on improving

Cited by 3 publications

References 4 publications

Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis

Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis

Analysing and Visualizing Tweets for U.S. President Popularity

Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App

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