Sentiment-oriented contextual advertising

Fan, Teng-Kai; Chang, Chia‐Hui

doi:10.1007/s10115-009-0222-2

Cited by 52 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BERT models use a multi-layer bidirectional transformer encoder with 6 layers in the encoder. For each word entered into the attention layer of the model, it is converted into Query (Q), Key (K) and Value (V) representing the actual word, keyword or meaning of the word and the value of the intent, purpose or polarity of the word respectively [37,38]. The finetuning and evaluation process deals basically with the selection of hyperparameters and compares experimentally the results of training and testing.…”

Section: Return Processedtweetmentioning

confidence: 99%

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Wusu¹,

Olabanjo²,

Padonu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concerning election and other trending subjects of interest. The outcome of the just concluded Presidential election in Nigeria shifts the focus on Lagos State governorship election. In this study, we propose a Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State Nigeria using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies. The preprocessed tweets were passed into the pretrained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also analyzed. The parameter-tuning yield different results with different learning rates (LR). Results showed that the learning rate at 1e-7 gave the best performance and that the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy.

show abstract

Section: Return Processedtweetmentioning

confidence: 99%

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Wusu¹,

Olabanjo²,

Padonu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…NLP is a field at the intersection of computer science, AI, and linguistics that aims to promote using computational methods to analyse language [10]. The field of Information Systems focuses on the application of such techniques to realworld problems [11] like social media analytics [12] and sentiment analysis [13]. "LLM" is an umbrella term for deep learning models for NLP, developed using the Transformer architecture [14].…”

Section: A Large Language Modelsmentioning

confidence: 99%

Intertwining Two Artificial Minds: Chaining GPT and RoBERTa for Emotion Detection

Kok-Shun,

Chan,

Peko

et al. 2023

2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

View full text Add to dashboard Cite

Emotion detection is a growing field focusing on developing machine learning models for classifying emotions. Identifying human emotions is paramount to understanding the deeper meaning of data, and is essential in many applications to improve their interaction with people. The advent of Large Language Models like GPT and RoBERTa models has enabled practitioners to understand textual data with more detail than before. In emotion detection, GPT models are found to excel at nuanced emotion detection and generation but lack the accuracy of fine-tuned models. Fine-tuned models are more accurate due to the domain-specific knowledge incorporated through training. This paper presents a novel framework combining the two types of models. It leverages the generative strength of GPT models for synthetic data augmentation on minority classes to improve the accuracy of a fine-tuned RoBERTa model. The RoBERTa, GPT-3.5-turbo, and GPT-4 evaluated on the GoEmotions dataset exhibited Macro-F1 scores of 0.49, 0.17, and 0.22, respectively. Our best-performing LangChain GPT-3.5 + RoBERTa model with synthetic data augmentation on minority classes achieved a Macro-F1 score of 0.51.

show abstract

“…Blogs have been a relevant source of data in sentiment analysis with posts containing reviews and comments. Fan et al [24] analyzed blog text to improve the quality of advertisements in the blogs that were more relevant to the user. To find the blogger's overall emotions towards any particular topic, Kuo et al [25] create a social opinion graph as generally every blogger is somewhat influenced by its social circle.…”

Section: Related Workmentioning

confidence: 99%

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

et al. 2020

View full text Add to dashboard Cite

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

show abstract

Sentiment-oriented contextual advertising

Cited by 52 publications

References 26 publications

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Intertwining Two Artificial Minds: Chaining GPT and RoBERTa for Emotion Detection

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Contact Info

Product

Resources

About