Sentiment Analysis of Economic Text: A Lexicon-Based Approach
- URL: http://arxiv.org/abs/2411.13958v1
- Date: Thu, 21 Nov 2024 09:13:12 GMT
- Title: Sentiment Analysis of Economic Text: A Lexicon-Based Approach
- Authors: Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Luca Tiozzo Pezzoli, Elisa Tosetti,
- Abstract summary: Economic lexicon (EL) specifically designed for textual applications in economics.
We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1].
The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment.
- Score: 0.7759447374181353
- License:
- Abstract: We propose an Economic Lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms used in documents discussing economic concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. We illustrate the use of the EL in the context of a simple sentiment measure and consider several applications in economics. The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment.
Related papers
- EconNLI: Evaluating Large Language Models on Economics Reasoning [22.754757518792395]
Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice.
We propose a new dataset, natural language inference on economic events (EconNLI), to evaluate LLMs' knowledge and reasoning abilities in the economic domain.
Our experiments reveal that LLMs are not sophisticated in economic reasoning and may generate wrong or hallucinated answers.
arXiv Detail & Related papers (2024-07-01T11:58:24Z) - Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study
on Speech Emotion Recognition [54.952250732643115]
We study Acoustic Word Embeddings (AWEs), a fixed-length feature derived from continuous representations, to explore their advantages in specific tasks.
AWEs have previously shown utility in capturing acoustic discriminability.
Our findings underscore the acoustic context conveyed by AWEs and showcase the highly competitive Speech Emotion Recognition accuracies.
arXiv Detail & Related papers (2024-02-04T21:24:54Z) - PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and
Entailment Recognition [63.51569687229681]
We argue for the need to recognize the textual entailment relation of each proposition in a sentence individually.
We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters.
Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document.
arXiv Detail & Related papers (2022-12-21T04:03:33Z) - Forecasting with Economic News [0.9281671380673304]
We consider only the text in the article that is semantically dependent on a term of interest.
We find that several measures of economic sentiment track closely business cycle fluctuations.
We also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.
arXiv Detail & Related papers (2022-03-29T15:46:42Z) - Taxonomy Enrichment with Text and Graph Vector Representations [61.814256012166794]
We address the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy.
We present a new method that allows achieving high results on this task with little effort.
We achieve state-of-the-art results across different datasets and provide an in-depth error analysis of mistakes.
arXiv Detail & Related papers (2022-01-21T09:01:12Z) - The AI Economist: Optimal Economic Policy Design via Two-level Deep
Reinforcement Learning [126.37520136341094]
We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework.
The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt.
In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
arXiv Detail & Related papers (2021-08-05T17:42:35Z) - A Comparison of Latent Semantic Analysis and Correspondence Analysis for
Text Mining [0.0]
Both latent semantic analysis (LSA) and correspondence analysis (CA) use a singular value decomposition (SVD) for dimensionality reduction.
In this article, LSA and CA are compared from a theoretical point of view and applied in both a toy example and an authorship attribution example.
arXiv Detail & Related papers (2021-07-25T09:10:10Z) - Automatic Construction of Context-Aware Sentiment Lexicon in the
Financial Domain Using Direction-Dependent Words [6.664755699733471]
We construct a lexicon named Senti-DD for the Sentiment lexicon composed of Direction-Dependent words.
Experiment results show that higher classification performance is achieved with Senti-DD.
arXiv Detail & Related papers (2021-06-10T13:08:00Z) - XAI Method Properties: A (Meta-)study [0.0]
We summarize the most cited and current in order to highlight the essential aspects of the state-of-the-art in XAI.
We illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly.
arXiv Detail & Related papers (2021-05-15T09:52:00Z) - Speakers Fill Lexical Semantic Gaps with Context [65.08205006886591]
We operationalise the lexical ambiguity of a word as the entropy of meanings it can take.
We find significant correlations between our estimate of ambiguity and the number of synonyms a word has in WordNet.
This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
arXiv Detail & Related papers (2020-10-05T17:19:10Z) - Learning from Learning Machines: Optimisation, Rules, and Social Norms [91.3755431537592]
It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making.
Recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
arXiv Detail & Related papers (2019-12-29T17:42:06Z)
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.