功能层面的细粒度情绪分析使用增强的长期短期记忆与即兴的本地搜索鱼优化
Lakshmi Revathi Krosuri1, Rama Satish Aravapalli1
1Vellore Institute of Technology University, Guntur, Andhra Pradesh, India.
PeerJ. Computer science
|June 22, 2023
概括
一个新的即兴本地搜索鱼优化增强的长短期记忆 (ILW-LSTM) 模型准确地分类在线产品评论中的情绪. 这种先进的方法达到97%的准确性,在功能级别情绪分析中表现优于现有的算法.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在线消费者评论对电子商务至关重要,为产品感知提供了洞察力.
- 由于复杂的语言和数据变化,对这些评论的情绪分析面临挑战.
- 准确的情绪预测对于数字市场上的企业和消费者来说至关重要.
研究的目的:
- 提出一个新的即兴的本地搜索鱼优化改进的长期短期记忆 (ILW-LSTM) 模型.
- 为了增强在线产品评论的功能级别情绪分析.
- 准确地将情绪分为积极,消极,非常积极,非常消极和中立的类别.
主要方法:
- 一个涉及数据收集,预处理和特征提取的多阶段过程,使用修改的反向类频率算法 (LFMI).
- 功能选择使用基于征收飞行的mayfly优化算法 (LFMO) 进行.
- 在"提示云数据集"上使用拟议的ILW-LSTM模型实现情绪分类.
主要成果:
- ILW-LSTM模型在情绪分类方面实现了97%的高精度.
- 使用准确性,回忆力,精度和F1得分的性能评估证明了该模型的有效性.
- 拟议的ILW-LSTM模型在特征级情绪分析中显著超过了其他领先的算法.
结论:
- ILW-LSTM模型提供了一个强大而准确的解决方案,用于在线产品评论的功能级别情绪分析.
- 该研究强调了整合鱼优化和LSTM在复杂情绪分类任务中的有效性.
- 这种方法为电子商务平台和消费者理解提供了宝贵的见解.
更多相关视频
05:57Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
Published on: April 8, 2019
6.9K
06:19Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
Published on: August 16, 2024
456
相关概念视频
Improving Translational Accuracy
2.6K
2.6K
Survival Tree
117
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
117
Long-term Potentiation
2.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
Hebbian LTP
LTP can occur when...
2.8K
Quantifying and Rejecting Outliers: The Grubbs Test
1.7K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.7K
Weighted Mean
5.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.2K
