Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies

• URL: http://arxiv.org/abs/2309.02045v2
• Date: Sun, 7 Jan 2024 14:59:15 GMT
• Title: Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies
• Authors: Yajing Wang and Zongwei Luo
• Abstract summary: We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis. 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.
• Score: 1.335032286337391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation [16.350747493026432]
  The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs) We propose the textbfStrategic Chain-of-Thought (SCoT) to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps. SCoT employs a two-stage approach within a single prompt: first eliciting an effective problem-solving strategy, which is then used to guide the generation of high-quality CoT paths and final answers.
  arXiv  Detail & Related papers  (2024-09-05T06:28:05Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies [0.5459032912385802]
  We investigate the effectiveness of Large Language Models (LLMs) in predicting entity-specific sentiment from political news articles. We employ a chain-of-thought (COT) approach augmented with rationale in few-shot in-context learning. We find that learning in-context significantly improves model performance, while the self-consistency mechanism enhances consistency in sentiment prediction.
  arXiv  Detail & Related papers  (2024-04-05T19:14:38Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)
• K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning [76.3114831562989]
  It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. We propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)"
  arXiv  Detail & Related papers  (2024-02-02T16:07:05Z)
• StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving [76.5322280307861]
  StrategyLLM allows LLMs to perform inductive reasoning, deriving general strategies from specific task instances, and deductive reasoning, applying these general strategies to particular task examples, for constructing generalizable and consistent few-shot prompts. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2% $rightarrow$ 38.8%), commonsense reasoning (70.3% $rightarrow$ 72.5%), algorithmic reasoning (73.7% $rightarrow$ 85.0
  arXiv  Detail & Related papers  (2023-11-15T09:18:09Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• Towards Better Chain-of-Thought Prompting Strategies: A Survey [60.75420407216108]
  Chain-of-Thought (CoT) shows its impressive strength when used as a prompting strategy for large language models (LLM) Recent years, the prominent effect of CoT prompting has attracted emerging research. This survey could provide an overall reference on related research.
  arXiv  Detail & Related papers  (2023-10-08T01:16:55Z)
• Analyzing Different Expert-Opined Strategies to Enhance the Effect on the Goal of a Multi-Attribute Decision-Making System Using a Concept of Effort Propagation and Application in Enhancement of High School Students' Performance [0.0]
  This paper proposes two such strategies, namely parallel and hierarchical effort assignment, and propagation strategies. The strategies are analyzed for a real-life case study regarding Indian high school administrative factors that play an important role in enhancing students' performance.
  arXiv  Detail & Related papers  (2023-07-05T12:53:40Z)
• Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies [20.15851744895469]
  In-context learning (ICL) has emerged as a new approach to various natural language processing tasks. In this paper, we aim to extend this method to question answering tasks that utilize structured knowledge sources.
  arXiv  Detail & Related papers  (2023-05-21T22:44:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.