ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS
This study adopts a systematic literature review to analyze social media sentiment towards specific commodities to enhance the accuracy of price movement forecasts in commodity markets. Drawing from the field of applied mathematics, the research gathered literature from Scopus, DOAJ, and Google Scho...
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| Format: | Article |
| Language: | English |
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Universitas Pattimura
2025-01-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13086 |
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| author | Mariono Mariono Syaharuddin Syaharuddin Sameer Ashraf Sunday Emmanuel Fadugba |
| author_facet | Mariono Mariono Syaharuddin Syaharuddin Sameer Ashraf Sunday Emmanuel Fadugba |
| author_sort | Mariono Mariono |
| collection | DOAJ |
| description | This study adopts a systematic literature review to analyze social media sentiment towards specific commodities to enhance the accuracy of price movement forecasts in commodity markets. Drawing from the field of applied mathematics, the research gathered literature from Scopus, DOAJ, and Google Scholar databases, covering publications from 2014 to 2024. A rigorous search strategy yielded 66 journal articles, with 30 being selected for their close relevance to keywords such as "social media sentiment," "commodity markets," and "price forecasting." Results indicate that social media sentiment significantly influences commodity prices, with particular variations based on commodity type and geographical context. Specific sentiment factors—especially intensity, polarity, and timing—were found to have a pronounced impact on price dynamics, with sentiment polarity being particularly influential in volatile markets. Additionally, advanced analytical methods, like Bayesian Dynamic Linear Models and LSTM neural networks, enhance predictive accuracy when applied to sentiment analysis in this context. These findings underscore the value of social media sentiment in refining forecasting models, while also highlighting gaps in understanding regional sentiment variations and their effects on different commodity types. By synthesizing these insights, this study emphasizes the importance of considering social media sentiment for more accurate price predictions and identifies key areas for future research to explore the multifaceted impacts of sentiment in commodity markets. |
| format | Article |
| id | doaj-art-2750fd461dc741d89600d37daa06795d |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-2750fd461dc741d89600d37daa06795d2025-08-20T03:37:34ZengUniversitas PattimuraBarekeng1978-72272615-30172025-01-0119119921410.30598/barekengvol19iss1pp199-21413086ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETSMariono Mariono0Syaharuddin Syaharuddin1Sameer Ashraf2Sunday Emmanuel Fadugba3Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Muhammadyah Mataram, IndonesiaDepartment of Mathematics Education, Faculty of Teacher Training and Education, Universitas Muhammadyah Mataram, IndonesiaDepartment of Mathematics, Faculty of Sciences, Thal University Bhakkar, PakistanDepartment of Mathematics, Faculty of Science, Ekiti State University, NigeriaThis study adopts a systematic literature review to analyze social media sentiment towards specific commodities to enhance the accuracy of price movement forecasts in commodity markets. Drawing from the field of applied mathematics, the research gathered literature from Scopus, DOAJ, and Google Scholar databases, covering publications from 2014 to 2024. A rigorous search strategy yielded 66 journal articles, with 30 being selected for their close relevance to keywords such as "social media sentiment," "commodity markets," and "price forecasting." Results indicate that social media sentiment significantly influences commodity prices, with particular variations based on commodity type and geographical context. Specific sentiment factors—especially intensity, polarity, and timing—were found to have a pronounced impact on price dynamics, with sentiment polarity being particularly influential in volatile markets. Additionally, advanced analytical methods, like Bayesian Dynamic Linear Models and LSTM neural networks, enhance predictive accuracy when applied to sentiment analysis in this context. These findings underscore the value of social media sentiment in refining forecasting models, while also highlighting gaps in understanding regional sentiment variations and their effects on different commodity types. By synthesizing these insights, this study emphasizes the importance of considering social media sentiment for more accurate price predictions and identifies key areas for future research to explore the multifaceted impacts of sentiment in commodity markets.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13086commodity marketsprice forecastingsocial media sentiment |
| spellingShingle | Mariono Mariono Syaharuddin Syaharuddin Sameer Ashraf Sunday Emmanuel Fadugba ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS Barekeng commodity markets price forecasting social media sentiment |
| title | ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS |
| title_full | ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS |
| title_fullStr | ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS |
| title_full_unstemmed | ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS |
| title_short | ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS |
| title_sort | analyzing social media sentiment toward specific commodities for forecasting price movements in commodity markets |
| topic | commodity markets price forecasting social media sentiment |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/13086 |
| work_keys_str_mv | AT marionomariono analyzingsocialmediasentimenttowardspecificcommoditiesforforecastingpricemovementsincommoditymarkets AT syaharuddinsyaharuddin analyzingsocialmediasentimenttowardspecificcommoditiesforforecastingpricemovementsincommoditymarkets AT sameerashraf analyzingsocialmediasentimenttowardspecificcommoditiesforforecastingpricemovementsincommoditymarkets AT sundayemmanuelfadugba analyzingsocialmediasentimenttowardspecificcommoditiesforforecastingpricemovementsincommoditymarkets |