1,586 research outputs found
Aspect-based Sentiment Analysis on Car Reviews Using SpaCy Dependency Parsing and VADER
All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed
Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies
Large Language Models (LLMs) have made significant strides in both scientific
research and practical applications. Existing studies have demonstrated the
state-of-the-art (SOTA) performance of LLMs in various natural language
processing tasks. However, the question of how to further enhance LLMs'
performance in specific task using prompting strategies remains a pivotal
concern. This paper explores the enhancement of LLMs' performance in sentiment
analysis through the application of prompting strategies. We formulate the
process of prompting for sentiment analysis tasks and introduce two novel
strategies tailored for sentiment analysis: RolePlaying (RP) prompting and
Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT
prompting strategy which is a combination of RP prompting and CoT prompting. We
conduct comparative experiments on three distinct domain datasets to evaluate
the effectiveness of the proposed sentiment analysis strategies. The results
demonstrate that the adoption of the proposed prompting strategies leads to a
increasing enhancement in sentiment analysis accuracy. Further, the CoT
prompting strategy exhibits a notable impact on implicit sentiment analysis,
with the RP-CoT prompting strategy delivering the most superior performance
among all strategies
Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis
Multimodal Sentiment Analysis (MSA) has been a popular topic in natural
language processing nowadays, at both sentence and aspect level. However, the
existing approaches almost require large-size labeled datasets, which bring
about large consumption of time and resources. Therefore, it is practical to
explore the method for few-shot sentiment analysis in cross-modalities.
Previous works generally execute on textual modality, using the prompt-based
methods, mainly two types: hand-crafted prompts and learnable prompts. The
existing approach in few-shot multi-modality sentiment analysis task has
utilized both methods, separately. We further design a hybrid pattern that can
combine one or more fixed hand-crafted prompts and learnable prompts and
utilize the attention mechanisms to optimize the prompt encoder. The
experiments on both sentence-level and aspect-level datasets prove that we get
a significant outperformance
TRUMP’S TWITTER EFFECT ON FINANCIAL INDEXES
This study investigates the impact of Trump’s tweets on abnormal returns and trading volumes of the S&P 500, using VADER to determine the sentiment of the daily tweets to identify relevant events. Based on the daily tweets from U.S President Donald Trump’s twitter account from 1st January 2018 to 16th December 2019, about 20 event samples had been identified. Statistical analysis using event study techniques demonstrated that only negative tweets could lead to statistically significant abnormal return and trading volumes over 1 or 2 trading days after the tweets. The study did not find any statistically significant relationship among positive tweets, abnormal returns, and trading volumes. According to the analysis, the conclusion of these results demonstrates that Trump’s tweet is still another source of information used to predict the U.S stock market return
Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies
The need to find, access and extract information has been the motivation for many
different fields of research in the past few years. The fields such as Machine Learning,
Question Answering Systems, Semantic Web, etc. each tries to cover parts of the
mentioned problem. Each of these fields have introduced many different tools and
approaches which in many cases are multi-disciplinary, covering more than one of
these fields to provide solution for one or more of them. On the other hand, the
expansion of the Web with Web 2.0, gave researchers many new tools to extend
approaches to help users extract and find information faster and easier. Currently,
the size of e-commerce and online shopping, the extended use of search engines for
different purposes and the amount of collaboration for creating content on the Web
provides us with different possibilities and challenges which we address some of them
here
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