Related papers: Enhancing Game Review Sentiment Classification on …
Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is…
We present an advanced approach to mobile app review analysis aimed at addressing limitations inherent in traditional star-rating systems. Star ratings, although intuitive and popular among users, often fail to capture the nuanced feedback…
We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment…
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also…
Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This…
The increasing volume of online reviews has made possible the development of sentiment analysis models for determining the opinion of customers regarding different products and services. Until now, sentiment analysis has proven to be an…
LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved. That's the wrong model. People change their minds, and…
Visual rating is an essential capability of artificial intelligence (AI) for multi-dimensional quantification of visual content, primarily applied in ordinal regression (OR) tasks such as image quality assessment, facial age estimation, and…
We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings often fail to capture…
Large Language Models (LLMs) have been garnering significant attention of AI researchers, especially following the widespread popularity of ChatGPT. However, due to LLMs' intricate architecture and vast parameters, several concerns and…
We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM). In addition to the word sequence, the model takes as input pattern match features that were developed to reduce…
Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before…
Student's feedback is an important source of collecting students' opinions to improve the quality of training activities. Implementing sentiment analysis into student feedback data, we can determine sentiments polarities which express all…
Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization…
In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data…
As large language models (LLMs) advance across diverse tasks, the need for comprehensive evaluation beyond single metrics becomes increasingly important. To fully assess LLM intelligence, it is crucial to examine their interactive dynamics…
Forecasting winners in E-sports with real-time analytics has the potential to further engage audiences watching major tournament events. However, making such real-time predictions is challenging due to unpredictable variables within the…
The stakeholders' needs in sentiment analysis for various issues, whether positive or negative, are speed and accuracy. One new challenge in sentiment analysis tasks is the limited training data, which often leads to suboptimal machine…
This paper presents a comprehensive survey of sentiment analysis methods for movie reviews, a benchmark task that has played a central role in advancing natural language processing. We review the evolution of techniques from early…
Sentiment analysis on social media such as Twitter provides organizations and individuals an effective way to monitor public emotions towards them and their competitors. As a result, sentiment analysis has become an important and…