本文選題：情感分析　切入點(diǎn)：詞向量　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：文本的情感分析又稱為觀點(diǎn)挖掘,是通過(guò)文字針對(duì)人對(duì)于實(shí)體的情緒的分析,主要關(guān)注人通過(guò)文字所表達(dá)的積極或消極的情緒。本課題研究采用統(tǒng)計(jì)語(yǔ)言模型,以基于機(jī)器學(xué)習(xí)的方法,以基于詞向量的深度學(xué)習(xí)算法實(shí)現(xiàn)文本的特征提取,以分類器進(jìn)行文本的情感分類,實(shí)現(xiàn)文本的自動(dòng)情感分析。研究的主要工作包括文本特征提取算法的目標(biāo)函數(shù)優(yōu)化、參數(shù)尋優(yōu)算法的仿生策略優(yōu)化和文本情感分析的參數(shù)尋優(yōu),研究的主要?jiǎng)?chuàng)新點(diǎn)如下:(1)針對(duì)Doc2Vec算法的目標(biāo)函數(shù)以余弦相似度表征向量差異性的不足,提出一種目標(biāo)函數(shù)優(yōu)化的文本特征提取算法——T-Doc2Vec算法。T-Doc2Vec算法以擴(kuò)展的余弦相似度函數(shù)——Tonimoto系數(shù)作為向量相似度函數(shù),在余弦相似度函數(shù)的基礎(chǔ)上考慮了向量模的影響,能更細(xì)致的反映向量之間的差異程度。并通過(guò)IMDB數(shù)據(jù)集的測(cè)試實(shí)驗(yàn)驗(yàn)證了算法優(yōu)化的有效性。(2)針對(duì)標(biāo)準(zhǔn)鯨魚(yú)算法在收斂性和全局性方面的不足,提出一種仿生策略優(yōu)化的混合鯨魚(yú)算法(HBWOA),并通過(guò)基準(zhǔn)測(cè)試函數(shù)集的對(duì)比實(shí)驗(yàn)證明了該算法優(yōu)化的收斂性能。仿生策略優(yōu)化的混合鯨魚(yú)算法,通過(guò)混沌映射初始化種群和自適應(yīng)調(diào)整搜索策略實(shí)現(xiàn)鯨魚(yú)算法的仿生策略優(yōu)化,結(jié)合粒子群算法"認(rèn)知部分"的優(yōu)點(diǎn)對(duì)鯨魚(yú)算法收斂過(guò)程進(jìn)行改進(jìn)。(3)結(jié)合仿生策略優(yōu)化的混合鯨魚(yú)算法實(shí)現(xiàn)文本情感分析的參數(shù)尋優(yōu)。首先以目標(biāo)函數(shù)優(yōu)化的T-Doc2Vec算法作為文本情感分析的特征提取算法,然后通過(guò)仿生策略優(yōu)化的混合鯨魚(yú)算法對(duì)文本情感分析進(jìn)行參數(shù)尋優(yōu),優(yōu)化文本情感分析的性能表現(xiàn)。
[Abstract]:Text emotional analysis, also known as viewpoint mining, is based on text analysis of people's emotions for entities, focusing on positive or negative emotions expressed by people through text. Based on machine learning, text feature extraction is realized by depth learning algorithm based on word vector, and text emotion classification is carried out by classifier. The main work of the research includes the optimization of the objective function of the text feature extraction algorithm, the bionic strategy optimization of the parameter optimization algorithm and the parameter optimization of the text emotion analysis. The main innovation of the study is as follows: 1) aiming at the deficiency of the objective function of the Doc2Vec algorithm, the cosine similarity is used to characterize the difference of the vector. A text feature extraction algorithm based on objective function optimization, T-Doc2Vec algorithm. T-Doc2Vec algorithm, using extended cosine similarity function, Tonimoto coefficient as vector similarity function, is proposed. The influence of vector modules is considered on the basis of cosine similarity function. Can reflect the degree of difference between vectors more detailedly. And through the test of IMDB data set, the validity of algorithm optimization is verified. 2) aiming at the shortage of convergence and globality of the standard whale algorithm, A hybrid whale algorithm for bionic strategy optimization (HBWOAA) is proposed, and the convergence performance of the algorithm is proved by the comparison of benchmark function sets. Using chaotic mapping to initialize the population and adjust the search strategy adaptively to optimize the bionic strategy of whale algorithm. Combined with the advantages of particle swarm optimization (PSO), the convergence process of whale algorithm is improved. 3) combined with bionic strategy optimization, hybrid whale algorithm is used to optimize the parameters of text emotional analysis. Firstly, T-Doc2Vec, which is optimized by objective function, is used to optimize the parameters of text emotion analysis. Algorithm as a feature extraction algorithm for text emotional analysis, Then the parameters of text emotion analysis are optimized by hybrid whale algorithm, which is optimized by bionic strategy, and the performance of text emotion analysis is optimized.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1

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本文編號(hào)：1569631