Sentiment analysis on social media using VADER and LSTM to optimise the marketing strategy for SOE energy products
Bibliographic record
Abstract
Sentiment analysis, a key component of natural language processing and data mining, plays a pivotal role in extracting subjective insights from textual data, particularly on social media platforms. In response to the growing importance of digital engagement, understanding public sentiment has become essential for formulating effective marketing strategies. This study aims to enhance the marketing strategy of energy products in subsidiaries of State-Owned Enterprises (SOEs) by employing a hybrid sentiment analysis model that integrates the Valence Aware Dictionary and Sentiment Reasoner (VADER) with Long Short-Term Memory (LSTM) neural networks. Utilizing a mixed-method approach that combines both quantitative and qualitative analyses, the study collects and processes data from multiple social media sources to identify and classify consumer sentiment. The results demonstrate that the hybrid VADER-LSTM model achieves an accuracy rate of up to 84%, enabling a more nuanced interpretation of consumer opinions. These insights inform the development of data-driven, responsive, and targeted marketing strategies. Furthermore, the study highlights the significance of fostering interactive communication between companies and consumers to enhance the impact of digital marketing efforts. Theoretical implications include a contribution to the academic discourse on information systems and digital marketing, while practical outcomes offer valuable guidance for SOEs in adopting adaptive, sentiment-informed marketing approaches within the energy sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".