title: Expert system in natural language processing sentiment analysis
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Ð­ÐºÑÐ¿ÐµÑÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÐºÐ¾Ð½ÑÐµÐºÑÑÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
Creators: name: ÐÐ¾Ð½ÑÐ°Ð½ÑÑ ÐÑÑÐ¸ÐµÑÑÐµÑ, ÐÐ¸Ð³ÐµÐ»Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
Ð¿ÐµÑÐµÐºÐ»ÑÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð´Ð¾Ð²
ÑÐ¼ÐµÑÐ¸Ð²Ð°Ð½Ð¸Ðµ ÐºÐ¾Ð´Ð¾Ð²
Ð°Ð½Ð°Ð»Ð¸Ð· Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ð¹
Ð¼Ð½Ð¾Ð³Ð¾ÑÐ·ÑÑÐ¸Ðµ
Ð´Ð²ÑÑÐ·ÑÑÐ¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ·ÑÐºÐ°
Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ·ÑÐºÐ°
Ð²ÑÑÑÐ°Ð¸Ð²Ð°Ð½Ð¸Ðµ ÑÐ»Ð¾Ð²
ÑÐ·ÑÐºÐ¾Ð²ÑÐµ ÑÐµÑÑÑÑÑ
Ð¼ÐµÐ¶ÑÑÐ·ÑÐºÐ¾Ð²Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑÐ°Ð½ÑÑÐµÑÐ½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
natural language processing
codeswitching
code-mixing
sentiment analysis
multilingualism
bilingualism
deep learning
language identification
language modeling
word embeddings
language resources
cross-lingual
transfer learning

description: ÐÐµÑÐµÐºÐ»ÑÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð´Ð° Ð¸ ÑÐ¼ÐµÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð´Ð¾Ð² â ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð½ÑÐµ Ð»Ð¸Ð½Ð³Ð²Ð¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ²Ð»ÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿ÑÐµÐ´Ð¿Ð¾Ð»Ð°Ð³Ð°ÑÑ Ð¿Ð¾Ð¾ÑÐµÑÐµÐ´Ð½Ð¾Ðµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð´Ð²ÑÑ Ð¸Ð»Ð¸ Ð±Ð¾Ð»ÐµÐµ ÑÐ·ÑÐºÐ¾Ð² Ð¸Ð»Ð¸ ÑÐ·ÑÐºÐ¾Ð²ÑÑ ÑÐ°Ð·Ð½Ð¾Ð²Ð¸Ð´Ð½Ð¾ÑÑÐµÐ¹ Ð² Ð¿ÑÐµÐ´ÐµÐ»Ð°Ñ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¾ÑÐ´ÐµÐ»ÑÐ½ÑÐ¹ ÑÐ°Ð·Ð³Ð¾Ð²Ð¾Ñ Ð¸Ð»Ð¸ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ. Ð¡ ÑÐ°ÑÑÑÑÐµÐ¹ Ð¼Ð½Ð¾Ð³Ð¾ÑÐ·ÑÑÐ½Ð¾ÑÑÑÑ Ð¾Ð±ÑÐµÑÑÐ²Ð°, ÑÑÐ°Ð»Ð¾ ÐºÑÐ°Ð¹Ð½Ðµ Ð²Ð°Ð¶Ð½Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÑ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÐºÐ¾Ð´Ð¾Ð²Ð¾Ð¹ ÐºÐ¾Ð¼Ð¼ÑÑÐ°ÑÐ¸Ð¸, Ð¸ ÑÐ¼ÐµÑÐ°Ð½Ð½ÑÐ¹ ÐºÐ¾Ð´ Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ° (NLP). Ð­ÑÐ° Ð´Ð¸ÑÑÐµÑÑÐ°ÑÐ¸Ñ Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ð¹ ÐºÐ¾Ð´Ð¾Ð²ÑÑ Ð¸ ÑÐ¼ÐµÑÐ°Ð½Ð½ÑÑ ÐºÐ¾Ð´Ð¾Ð², ÑÐµÐºÑÑ Ð½Ð° Ð°Ð½Ð³Ð»Ð¸Ð¹ÑÐºÐ¾Ð¼ Ð¸ Ð¸ÑÐ¿Ð°Ð½ÑÐºÐ¾Ð¼ ÑÐ·ÑÐºÐ°Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ (RNN) Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð³Ð¾. Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½Ñ. Ð Ð´Ð¸ÑÑÐµÑÑÐ°ÑÐ¸Ð¸ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° ââÐºÐ¾Ð¼Ð¿Ð»ÐµÐºÑÐ½Ð°Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð»Ð¾Ð³Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ RNN, Ð¾ ÑÐµÐºÑÑÐµ Ñ ÐºÐ¾Ð´Ð¾Ð²Ð¾Ð¹ ÐºÐ¾Ð¼Ð¼ÑÑÐ°ÑÐ¸ÐµÐ¹ Ð¸ ÑÐ¼ÐµÑÐ°Ð½Ð½ÑÐ¼ ÐºÐ¾Ð´Ð¾Ð¼, Ð¾ÑÐ²Ð°ÑÑÐ²Ð°ÑÑÐµÐ¼ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð¿ÑÐ¾ÑÐµÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð¾ÑÐµÐ½ÐºÐ¸. ÐÐµÑÐ¾Ð´Ð¸ÐºÐ° Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÑÑÑ Ðº Ð½Ð°Ð±Ð¾ÑÑ Ð´Ð°Ð½Ð½ÑÑ IMDb, ÐºÐ¾ÑÐ¾ÑÑÐ¹ ÑÐ¾Ð´ÐµÑÐ¶Ð¸Ñ ÑÐ¸Ð»ÑÐ¼Ñ Ð¾Ð±Ð·Ð¾ÑÑ Ð½Ð° Ð°Ð½Ð³Ð»Ð¸Ð¹ÑÐºÐ¾Ð¼ ÑÐ·ÑÐºÐµ, Ð² ÐºÐ¾ÑÐ¾ÑÑÑ Ð½Ð°Ð±Ð»ÑÐ´Ð°ÐµÑÑÑ Ð¿ÐµÑÐµÐºÐ»ÑÑÐµÐ½Ð¸Ðµ Ð¸ ÑÐ¼ÐµÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð´Ð¾Ð² Ð¼ÐµÐ¶Ð´Ñ Ð°Ð½Ð³Ð»Ð¸Ð¹ÑÐºÐ¸Ð¼Ð¸ ÑÐ·ÑÐºÐ°Ð¼Ð¸, Ð¸ Ð¸ÑÐ¿Ð°Ð½ÑÐºÐ¸Ð¹. Ð¦ÐµÐ»Ñ ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð² ÑÐ¾Ð¼, ÑÑÐ¾Ð±Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÑ Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¾Ð±Ð·Ð¾ÑÐ° ÑÐ¸Ð»ÑÐ¼Ð°. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ð¹ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ð¼Ð¸ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ°Ð¼Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ Ð°Ð½Ð³Ð»Ð¸Ð¹ÑÐºÐ¾Ð¼ Ð¸ Ð¸ÑÐ¿Ð°Ð½ÑÐºÐ¾Ð¼ ÑÐ·ÑÐºÐ°Ñ, Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐ¸. ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾, Ð² ÑÑÐ¾Ð¹ Ð´Ð¸ÑÑÐµÑÑÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð´ÑÐµÑÐºÐ¸Ð²Ð°ÐµÑÑÑ Ð¸Ð½Ð½Ð¾Ð²Ð°ÑÐ¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ RNN Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð· Ð½Ð°ÑÑÑÐ¾ÐµÐ½Ð¸Ð¹ ÑÐµÐºÑÑÐ° Ñ ÐºÐ¾Ð´Ð¾Ð²ÑÐ¼ Ð¿ÐµÑÐµÐºÐ»ÑÑÐµÐ½Ð¸ÐµÐ¼ Ð¸ ÑÐ¼ÐµÑÐ°Ð½Ð½ÑÐ¼ ÐºÐ¾Ð´Ð¾Ð¼ Ð¸ Ð¾Ð±ÑÑÐ¶Ð´Ð°ÐµÑ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÐµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÑÐ¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð² ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¾Ð±Ð»Ð°ÑÑÑÑ, ÑÐ°ÐºÐ¸Ñ ÐºÐ°Ðº Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¾ÑÐ¸Ð°Ð»ÑÐ½ÑÑ ÑÐµÑÐµÐ¹, Ð°Ð½Ð°Ð»Ð¸Ð· Ð¾ÑÐ·ÑÐ²Ð¾Ð² ÐºÐ»Ð¸ÐµÐ½ÑÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐµÐ¿Ð¾Ð´Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ ÑÐ·ÑÐºÐ°.
description: Code-switching and code-mixing are prevalent linguistic phenomena that involve the alternating use of two or more languages or language varieties within a single conversation or sentence. With the growing multilingual nature of societies, it has become crucial to develop tools and techniques for analyzing code-switched and code-mixed text in natural language processing (NLP). This thesis focuses on sentiment analysis of code-switched and code-mixed text in English and Spanish using recurrent neural networks (RNNs) as the primary analytical tool. The thesis presents a comprehensive methodology for training RNNs on code-switched and code-mixed text, covering the neural network architecture, training process, and evaluation metrics. The methodology is applied to the IMDb dataset, which contains movie reviews in English that exhibit code-switching and code-mixing between English and Spanish. The goal is to predict the sentiment of each movie review. The results of the sentiment analysis are compared with existing sentiment analysis tools for English and Spanish, demonstrating the effectiveness of the proposed methodology. Additionally, this thesis highlights the innovation of using RNNs for sentiment analysis of code-switched and code-mixed text and discusses the potential applications of this methodology in various domains, such as social media analysis, customer feedback analysis, and language teaching and learning.