{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:34:37Z","timestamp":1760060077212,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., \u2018price\u2019 and \u2018service\u2019), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework\u2019s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like \u2018service\u2019 and \u2018quality\u2019 are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion.<\/jats:p>","DOI":"10.3390\/info16080670","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Polarity: Forecasting Consumer Sentiment with Aspect- and Topic-Conditioned Time Series Models"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1457-3021","authenticated-orcid":false,"given":"Mian Usman","family":"Sattar","sequence":"first","affiliation":[{"name":"College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-837X","authenticated-orcid":false,"given":"Raza","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Science and Engineering, Southampton Solent University, Southampton SO14 0YN, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1168-2864","authenticated-orcid":false,"given":"Sellappan","family":"Palaniappan","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Digital Technology, HELP University, Kuala Lumpur 50490, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-4095","authenticated-orcid":false,"given":"Salman","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nazeer Hussain University, ST-2, Near Karimabad, Karachi 75950, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3034-0586","authenticated-orcid":false,"given":"Hamza Wazir","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Business Studies, Namal University, Mianwali 42250, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1007\/s13278-012-0079-3","article-title":"Social media and political communication: A social media analytics framework","volume":"3","author":"Stieglitz","year":"2013","journal-title":"Soc. 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