Sentiment Analysis
Sentiment analysis is the process of determining the sentiment of a piece of text, such as a tweet or a review.
Papers and Code
GERestaurant: A German Dataset of Annotated Restaurant Reviews for Aspect-Based Sentiment Analysis
Aug 15, 2024
We present GERestaurant, a novel dataset consisting of 3,078 German language restaurant reviews manually annotated for Aspect-Based Sentiment Analysis (ABSA). All reviews were collected from Tripadvisor, covering a diverse selection of restaurants, including regional and international cuisine with various culinary styles. The annotations encompass both implicit and explicit aspects, including all aspect terms, their corresponding aspect categories, and the sentiments expressed towards them. Furthermore, we provide baseline scores for the four ABSA tasks Aspect Category Detection, Aspect Category Sentiment Analysis, End-to-End ABSA and Target Aspect Sentiment Detection as a reference point for future advances. The dataset fills a gap in German language resources and facilitates exploration of ABSA in the restaurant domain.
Quantum-inspired Interpretable Deep Learning Architecture for Text Sentiment Analysis
Aug 15, 2024Text has become the predominant form of communication on social media, embedding a wealth of emotional nuances. Consequently, the extraction of emotional information from text is of paramount importance. Despite previous research making some progress, existing text sentiment analysis models still face challenges in integrating diverse semantic information and lack interpretability. To address these issues, we propose a quantum-inspired deep learning architecture that combines fundamental principles of quantum mechanics (QM principles) with deep learning models for text sentiment analysis. Specifically, we analyze the commonalities between text representation and QM principles to design a quantum-inspired text representation method and further develop a quantum-inspired text embedding layer. Additionally, we design a feature extraction layer based on long short-term memory (LSTM) networks and self-attention mechanisms (SAMs). Finally, we calculate the text density matrix using the quantum complex numbers principle and apply 2D-convolution neural networks (CNNs) for feature condensation and dimensionality reduction. Through a series of visualization, comparative, and ablation experiments, we demonstrate that our model not only shows significant advantages in accuracy and efficiency compared to previous related models but also achieves a certain level of interpretability by integrating QM principles. Our code is available at QISA.
A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification
Aug 15, 2024The versatile nature of Visual Sentiment Analysis (VSA) is one reason for its rising profile. It isn't easy to efficiently manage social media data with visual information since previous research has concentrated on Sentiment Analysis (SA) of single modalities, like textual. In addition, most visual sentiment studies need to adequately classify sentiment because they are mainly focused on simply merging modal attributes without investigating their intricate relationships. This prompted the suggestion of developing a fusion of deep learning and machine learning algorithms. In this research, a deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. Furthermore, gradient boosting algorithm has been used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED. Finally, cutting-edge deep learning and machine learning models were used to compare the proposed strategy. When compared to state-of-the-art approaches, the proposed method demonstrates exceptional performance on the datasets presented.
WiDe-analysis: Enabling One-click Content Moderation Analysis on Wikipedia's Articles for Deletion
Aug 10, 2024Content moderation in online platforms is crucial for ensuring activity therein adheres to existing policies, especially as these platforms grow. NLP research in this area has typically focused on automating some part of it given that it is not feasible to monitor all active discussions effectively. Past works have focused on revealing deletion patterns with like sentiment analysis, or on developing platform-specific models such as Wikipedia policy or stance detectors. Unsurprisingly, however, this valuable body of work is rather scattered, with little to no agreement with regards to e.g., the deletion discussions corpora used for training or the number of stance labels. Moreover, while efforts have been made to connect stance with rationales (e.g., to ground a deletion decision on the relevant policy), there is little explanability work beyond that. In this paper, we introduce a suite of experiments on Wikipedia deletion discussions and wide-analyis (Wikipedia Deletion Analysis), a Python package aimed at providing one click analysis to content moderation discussions. We release all assets associated with wide-analysis, including data, models and the Python package, and a HuggingFace space with the goal to accelerate research on automating content moderation in Wikipedia and beyond.
An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting
Aug 09, 2024This research examines the use of Large Language Models (LLMs) in predicting time series, with a specific focus on the LLMTIME model. Despite the established effectiveness of LLMs in tasks such as text generation, language translation, and sentiment analysis, this study highlights the key challenges that large language models encounter in the context of time series prediction. We assess the performance of LLMTIME across multiple datasets and introduce classical almost periodic functions as time series to gauge its effectiveness. The empirical results indicate that while large language models can perform well in zero-shot forecasting for certain datasets, their predictive accuracy diminishes notably when confronted with diverse time series data and traditional signals. The primary finding of this study is that the predictive capacity of LLMTIME, similar to other LLMs, significantly deteriorates when dealing with time series data that contain both periodic and trend components, as well as when the signal comprises complex frequency components.
Enhanced Semantic Graph Based Approach With Sentiment Analysis For User Interest Retrieval From Social Sites
Aug 08, 2024Blogs and social networking sites serve as a platform to the users for expressing their interests, ideas and thoughts. Targeted marketing uses the recommendation systems for suggesting their services and products to the users or clients. So the method used by target marketing is extraction of keywords and main topics from the user generated texts. Most of conventional methods involve identifying the personal interests just on the basis of surveys and rating systems. But the proposed research differs in manner that it aim at using the user generated text as a source medium for identifying and analyzing the personal interest as a knowledge base area of users. Semantic graph based approach is proposed research work that identifies the references of clients and users by analyzing their own texts such as tweets. The keywords need to be extracted from the text generated by the user on the social networking sites. This can be made possible by using several algorithms that extracts the keywords automatically from the available content provided by the user. Based on frequency and degree it ranks the extracted keywords. Furthermore, semantic graph based model assists in providing useful suggestions just by extracting the interests of users by analyzing their contents from social media. In this approach graph comprises of nodes and edges where nodes represents the keywords extracted by the algorithm and edges shows the semantic connection between the nodes. The method does not require internet related user activities like surveys or ratings to gather user interest related information.
Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective
Aug 08, 2024In the internet era, almost every business entity is trying to have its digital footprint in digital media and other social media platforms. For these entities, word of mouse is also very important. Particularly, this is quite crucial for the hospitality sector dealing with hotels, restaurants etc. Consumers do read other consumers reviews before making final decisions. This is where it becomes very important to understand which aspects are affecting most in the minds of the consumers while giving their ratings. The current study focuses on the consumer reviews of Indian hotels to extract aspects important for final ratings. The study involves gathering data using web scraping methods, analyzing the texts using Latent Dirichlet Allocation for topic extraction and sentiment analysis for aspect-specific sentiment mapping. Finally, it incorporates Random Forest to understand the importance of the aspects in predicting the final rating of a user.
Are Social Sentiments Inherent in LLMs? An Empirical Study on Extraction of Inter-demographic Sentiments
Aug 08, 2024Large language models (LLMs) are supposed to acquire unconscious human knowledge and feelings, such as social common sense and biases, by training models from large amounts of text. However, it is not clear how much the sentiments of specific social groups can be captured in various LLMs. In this study, we focus on social groups defined in terms of nationality, religion, and race/ethnicity, and validate the extent to which sentiments between social groups can be captured in and extracted from LLMs. Specifically, we input questions regarding sentiments from one group to another into LLMs, apply sentiment analysis to the responses, and compare the results with social surveys. The validation results using five representative LLMs showed higher correlations with relatively small p-values for nationalities and religions, whose number of data points were relatively large. This result indicates that the LLM responses including the inter-group sentiments align well with actual social survey results.
Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models
Aug 08, 2024Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. For affective computing systems, an understanding of how users regulate their emotions can be highly useful, for example to provide feedback in job interview training, or in psychotherapeutic scenarios. However, at present no method to automatically classify different emotion regulation strategies in a cross-user scenario exists. At the same time, recent studies showed that instruction-tuned Large Language Models (LLMs) can reach impressive performance across a variety of affect recognition tasks such as categorical emotion recognition or sentiment analysis. While these results are promising, it remains unclear to what extent the representational power of LLMs can be utilized in the more subtle task of classifying users' internal emotion regulation strategy. To close this gap, we make use of the recently introduced \textsc{Deep} corpus for modeling the social display of the emotion shame, where each point in time is annotated with one of seven different emotion regulation classes. We fine-tune Llama2-7B as well as the recently introduced Gemma model using Low-rank Optimization on prompts generated from different sources of information on the \textsc{Deep} corpus. These include verbal and nonverbal behavior, person factors, as well as the results of an in-depth interview after the interaction. Our results show, that a fine-tuned Llama2-7B LLM is able to classify the utilized emotion regulation strategy with high accuracy (0.84) without needing access to data from post-interaction interviews. This represents a significant improvement over previous approaches based on Bayesian Networks and highlights the importance of modeling verbal behavior in emotion regulation.
Decoding Visual Sentiment of Political Imagery
Aug 07, 2024How can we define visual sentiment when viewers systematically disagree on their perspectives? This study introduces a novel approach to visual sentiment analysis by integrating attitudinal differences into visual sentiment classification. Recognizing that societal divides, such as partisan differences, heavily influence sentiment labeling, we developed a dataset that reflects these divides. We then trained a deep learning multi-task multi-class model to predict visual sentiment from different ideological viewpoints. Applied to immigration-related images, our approach captures perspectives from both Democrats and Republicans. By incorporating diverse perspectives into the labeling and model training process, our strategy addresses the limitation of label ambiguity and demonstrates improved accuracy in visual sentiment predictions. Overall, our study advocates for a paradigm shift in decoding visual sentiment toward creating classifiers that more accurately reflect the sentiments generated by humans.