A tight linear chromatic bound for (P3∪P2,W4P_3\cup P_2, W_4)-free graphs

For two vertex disjoint graphs $H$ and $F$, we use $H\cup F$ to denote the graph with vertex set $V(H)\cup V(F)$ and edge set $E(H)\cup E(F)$, and use $H+F$ to denote the graph with vertex set $V(H)\cup V(F)$ and edge set $E(H)\cup E(F)\cup\{xy\;|\; x\in V(H), y\in V(F)$$\}$. A $W_4$ is the graph $K_1+C_4$. In this paper, we prove that $\chi(G)\le 2\omega(G)$ if $G$ is a ($P_3\cup P_2, W_4$)-free graph. This bound is tight when $\omega =2$ and $3$, and improves the main result of Wang and Zhang. Also, this bound partially generalizes some results of Prashant {\em et al.}.Comment: arXiv admin note: text overlap with arXiv:2308.05442, arXiv:2307.1194

Sentiment Analysis of Tourism Reviews: An exploratory study based on CNNs built on LSTM model

This study is to develop a sentiment analysis system for customers’ review on a scenic site. It is based on Convolutional Neural Networks (CNNs) built on Long Short-Term Memory (LSTM) models for text feature extraction under a deep learning framework. The CNNs built on LSTM models applies convolutional filters of CNNs repeatedly operate on the output matrix of LSTM to obtain robust text feature vector. In this study, the optimal parameter configurations for each component of CNNs and LSTM are given individually in the first place. Then, the entire optimal parameter configuration for the integration recognition frame of the system is identified around the optimum of each component. The results demonstrate that, by employing such a method, the accuracy for sentiment analysis with CNNs built on LSTM model, compared with a single CNNs or LSTM model, is improved by 3.13% and 1.71% respectively