How Dimensionality Reduction Affects Sentiment Analysis NLP Tasks: An Experimental Study

Akritidis, Leonidas; Bozanis, Panayiotis

doi:10.1007/978-3-031-08337-2_25

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Advanced feature techniques and data treatments were employed to enhance the model performance further, following the guidelines presented in the extant literature [54,55]. Our methodology emphasized efficient text representation and low-dimensional dense text vectors for capturing nuanced semantic relationships in text.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Almeida,

Castro,

Leiva

et al. 2024

JTAER

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Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces a bespoke and integrated approach to analyzing customer sentiment, with a particular emphasis on a case study in the Portuguese retail market. Capitalizing on the strengths of SentiLex-PT, a sentiment lexicon curated for the Portuguese language, and an array of sophisticated machine learning algorithms, this research constructs advanced models that encapsulate both lexical features and the subtleties of linguistic composition. A meticulous comparative analysis singles out multinomial logistic regression as the pre-eminent model for its applicability and accuracy within our case study. The findings of this analysis highlight the pivotal role that sentiment data play in strategic decision-making processes such as reputation management, strategic planning, and forecasting market trends within the retail sector. To the extent of our knowledge, this work is pioneering in its provision of a holistic sentiment analysis framework tailored to the Portuguese retail context, marking an advancement for both the academic field and industry application.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Almeida,

Castro,

Leiva

et al. 2024

JTAER

View full text Add to dashboard Cite

show abstract

“…The rise of social media has transformed societal interaction, enabling a digital landscape where "online individualism" continues to increase, enhancing dialog and collective action. This digital era emphasizes the importance of sentiment analysis, which aims to automate the extraction of subjective information-opinions, feelings, and attitudes-from natural language texts [56][57][58][59]. In financial contexts, sentiment reflects market participants' collective optimism or pessimism, significantly influencing asset prices.…”

Section: The Role Of Sentiment Analysis In Stock Market Forecastingmentioning

confidence: 99%

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Papageorgiou,

Gkaimanis,

Tjortjis

2024

Electronics

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Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

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“…A. NVivo for script analysis [10] : Qualitative statistical analysis system NVivo reviews "Friends" episodes. This program measures debate, television time, and CR to assess relationships in the series.…”

Section: Specific Content Analysis Softwarementioning

confidence: 99%

Comparison of novel character relationship network mining and drama character relationship shaping algorithms

Xiang,

Abidin

2024

ESP

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This study embarks on an interdisciplinary journey to analyze and compare Character Relationships (CR) in two diverse storytelling mediums – the classic novel “Emma” by Jane Austen and the popular TV series “Friends”. Leveraging a blend of Natural Language Processing (NLP) and advanced video analysis tools, this research “sheds light on” the intricate network of CR within these narratives. This study scientifically analyzes these relationships' complete method, creation, and impact using sentiment analysis, object identification, and story coherence algorithms. Qualitative and quantitative metrics such as precision, recall, F-score, and accuracy assist in explaining character updates. The content and TV exhibit distinct storytelling modes, and this study demonstrates that algorithmic analysis of stories is practical. The findings of this study request to contribute to online studies by focusing on understanding character networks and their vital role in TV storytelling.

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How Dimensionality Reduction Affects Sentiment Analysis NLP Tasks: An Experimental Study

Cited by 7 publications

References 23 publications

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Comparison of novel character relationship network mining and drama character relationship shaping algorithms

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