Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative, highlighting disruptive ads and login issues, while 13.76% were neutral. LSI identified eight main topics, including user experience and ad complaints. Visualizations through heatmaps, distance maps, and word clouds emphasized terms like "news" and "ads." This research recommends that Detik.com developers reduce ad disruptions, fix technical problems, and understand frequently discussed topics to enhance user satisfaction.

Authors

Publication Details

Journal
Jurnal Teknologi Terapan G-Tech
Published
2024-10-17
DOI
https://doi.org/10.70609/gtech.v8i4.5434
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

Dedi I. Inan, Ratna Juita, Victor Arie lambadya Sirait, Glenzkovic Samuel Moniharapon
Jurnal Teknologi Terapan G-Tech
Technology and Data Analysis
article

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing

Dedi I. Inan, Ratna Juita, Victor Arie lambadya Sirait, Glenzkovic Samuel Moniharapon
article en

Abstract

The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative, highlighting disruptive ads and login issues, while 13.76% were neutral. LSI identified eight main topics, including user experience and ad complaints. Visualizations through heatmaps, distance maps, and word clouds emphasized terms like "news" and "ads." This research recommends that Detik.com developers reduce ad disruptions, fix technical problems, and understand frequently discussed topics to enhance user satisfaction.

Jurnal Teknologi Terapan G-TechVol. 8(4)
Openalex Percentile: Top 72%
Technology and Data Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analysis Sentiment & Topic Modelling using Latent Semantic Indexing — Dedi I. Inan, Ratna Juita, et al. · Jurnal Teknologi Terapan G-Tech (2024) | TGRS Research Map | TGRS