深度學習 情感分析

介紹 (Introduction)

The growth of the internet due to social networks such as Facebook, Twitter, Linkedin, Instagram etc. has led to significant users interaction and has empowered users to express their opinions about products, services, events, their preferences among others. It has also provided opportunities to the users to share their wisdom and experiences with each other. The faster development of social networks is causing explosive growth of digital content. It has turned online opinions, blogs, tweets, and posts into a very valuable asset for the corporates to get insights from the data and plan their strategy. Business organizations need to process and study these sentiments to investigate data and to gain business insights(Yadav & Vishwakarma, 2020). Traditional approach to manually extract complex features, identify which feature is relevant, and derive the patterns from this huge information is very time consuming and require significant human efforts. However, Deep Learning can exhibit excellent performance via Natural Language Processing (NLP) techniques to perform sentiment analysis on this massive information. The core idea of Deep Learning techniques is to identify complex features extracted from this vast amount of data without much external intervention using deep neural networks. These algorithms automatically learn new complex features. Both automatic feature extraction and availability of resources are very important when comparing the traditional machine learning approach and deep learning techniques(Araque et al., 2017). Here the goal is to classify the opinions and sentiments expressed by users. In this article, we will discuss about various sentiment analysis techniques and several ensemble models to aggregate the information from multiple features.

由于諸如Facebook，Twitter，Linkedin，Instagram等社交網絡的互聯網發展，導致了重要的用戶互動，并使用戶能夠表達其對產品，服務，事件，偏好的看法。 它還為用戶提供了彼此分享他們的智慧和經驗的機會。 社交網絡的快速發展正在引起數字內容的爆炸性增長。 它已將在線意見，博客，推文和帖子變成了非常有價值的資產，使企業可以從數據中獲得洞察力并計劃其戰略。 商業組織需要處理和研究這些情緒，以調查數據并獲得商業見解(Yadav和Vishwakarma，2020年)。 手動提取復雜特征，識別哪個特征相關并從大量信息中導出模式的傳統方法非常耗時，并且需要大量人力。 但是，深度學習可以通過自然語言處理(NLP)技術表現出出色的性能，從而對大量信息進行情感分析。 深度學習技術的核心思想是使用深層神經網絡無需大量外部干預即可識別從大量數據中提取的復雜特征。 這些算法會自動學習新的復雜功能。 在比較傳統的機器學習方法和深度學習技術時，自動特征提取和資源可用性都非常重要(Araque et al。，2017)。 這里的目標是對用戶表達的觀點和情感進行分類。 在本文中，我們將討論各種情感分析技術和幾種集成模型，以匯總來自多個功能的信息。

抽象 (Abstract)

Deep Learning uses powerful neural network algorithms to mimics the way human brain process data for translating languages, recognizing speech, detecting objects and making decisions. Deep Learning algorithms are able to identify and learn the patterns from both unstructured and unlabeled data without human intervention. Deep Learning techniques learn through multiple layers of representation and generate state of the art predictive results. In the past years, Deep Learning techniques have been very successful in performing the sentiment analysis. It provides automatic feature extraction, rich representation capabilities and better performance than traditional feature based techniques. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods(Preethi et al., 2017). Two techniques of neural networks are very common — Convolutional Neural Networks(CNN) for image processing and Recurrent Neural Networks (RNN) — for natural language processing (NLP) tasks(Goularas & Kamis, 2019). Deep Learning is used to optimize the recommendations depending on the sentiment analysis performed on the different reviews, which are taken from different social networking sites. The Experiments performed indicate that the RNN based Deep-learning Sentiment Analysis (RDSA) improvises the behavior by increasing the accuracy of the sentiment analysis, which in turn yields better recommendations to the user and thus helps to identify a particular position as per the requirement of the user need(Preethi et al., 2017). In this article, we will discuss popular deep learning models which are increasingly applied in the sentiment analysis including CNN, RNN, various ensemble techniques. This article provides insights on various techniques for sentiment analysis.

深度學習使用強大的神經網絡算法來模仿人腦處理數據以翻譯語言，識別語音，檢測物體并做出決策的方式。 深度學習算法無需人工干預即可從非結構化和未標記的數據中識別和學習模式。 深度學習技術通過多層表示進行學習，并生成最新的預測結果。 在過去的幾年中，深度學習技術在執行情感分析方面非常成功。 與傳統的基于特征的技術相比，它提供了自動特征提取，豐富的表示功能以及更好的性能。 這些歷史悠久的方法可以產生強大的基線，其預測能力可以與新興的深度學習方法結合使用(Preethi等人，2017)。 神經網絡的兩種技術非常普遍-用于圖像處理的卷積神經網絡(CNN)和用于自然語言處理(NLP)任務的遞歸神經網絡(RNN)(Goularas＆Kamis，2019)。 深度學習用于根據對不同評論的情感分析來優化建議，這些評論來自不同的社交網站。 進行的實驗表明，基于RNN的深度學習情感分析(RDSA)通過提高情感分析的準確性來改善行為，從而反過來為用戶提供更好的建議，從而有助于根據用戶的需求確定特定職位用戶需求(Preethi等人，2017)。 在本文中，我們將討論流行的深度學習模型，這些模型越來越廣泛地用于情感分析中，包括CNN，RNN和各種集成技術。 本文提供了各種情感分析技術的見解。

情緒分析 (Sentiment analysis)

It is a set of techniques / algorithms used to detect the sentiment (positive, negative, or neutral) of a given text. It is a very powerful application of natural language processing (NLP) and finds usage in a large number of industries. It refers to the use of NLP, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study different states and subjective information. The sentiment analysis sometimes goes beyond the categorization of texts to find opinions and categorizes them as positive or negative, desirable or undesirable. Below figure describes the architecture of sentiment classification on texts. In this, we modify the provided reviews by applying specific filters, and we use the prepared datasets by applying the parameters and implement our proposed model for evaluation(Ghorbani et al., 2020).

它是用于檢測給定文本的情緒(正，負或中性)的一組技術/算法。 它是自然語言處理 (NLP)的非常強大的應用程序，并在許多行業中得到了使用。 它指的是使用NLP， 文本分析 ， 計算語言學和生物識別技術來系統地識別，提取，量化和研究不同的狀態和主觀信息。 情感分析有時會超出文本的分類范圍，以找到觀點并將其歸類為正面或負面，理想或不良。 下圖描述了文本中情感分類的體系結構。 在這種情況下，我們通過應用特定的過濾器來修改提供的評論，并通過應用參數來使用準備好的數據集，并實施我們提出的評估模型(Ghorbani等，2020)。

Sentiment Classification Architecture情感分類架構

There are three approaches to perform sentiment analysis –

進行情感分析的方法有以下三種：

1. Lexicon based techniques — It can be classified in two types -

1.基于詞匯的技術 -可以分為兩種類型-

a. Dictionary based — In this approach, classification is done by using dictionary of terms, which can be found in WordNet or SentiWordNet.

一個。 基于字典-在這種方法中，分類是通過使用術語詞典完成的，可以在WordNet或SentiWordNet中找到它們。

b. Corpus based — In this approach, classification is done based on the statistical analysis of the content of group of documents using techniques such as hidden Markov models (HMM) , conditional random field (CRF), k-nearest neighbors (k-NN) among others.

b。 基于語料庫-在這種方法中，分類是基于對文檔組內容的統計分析，使用的技術包括隱馬爾可夫模型(HMM)，條件隨機場(CRF)，k個近鄰(k-NN)其他。

2. Machine learning based techniques — It can be classified in two groups –

2.基于機器學習的技術 -可以分為兩類-

a. Traditional Models — It refers to classical techniques of machine learning such as support vector machines , maximum entropy classifier, naive Bayes classifier. The inputs of these models includes sentiment lexicon based features, lexical features, parts of speech, adverbs and adjectives.

一個。 傳統模型-指機器學習的經典技術，例如支持向量機，最大熵分類器，樸素貝葉斯分類器。 這些模型的輸入包括基于情感詞典的功能，詞匯功能，詞性，副詞和形容詞。

b. Deep Learning Models — It provides more accurate results than traditional models. It includes models such as CNN, RNN, and DNN. These models address classification problems at document level, sentence level or aspect level.

b。 深度學習模型-比傳統模型提供更準確的結果。 它包括諸如CNN，RNN和DNN之類的模型。 這些模型解決了文檔級別，句子級別或方面級別的分類問題。

3. Hybrid Approach — It combine machine learning and lexicon based approaches. Sentiment lexicons plays a significant role within most of these approaches. Below figure illustrates taxonomy of various methods including deep-learning for sentiment analysis techniques. Sentiment analysis, whether performed by means of deep learning or traditional machine learning, requires that text training data be cleaned before being used to induce the classification(Dang et al., 2020).

3.混合方法 -它結合了機器學習和基于詞典的方法。 情感詞典在大多數這些方法中起著重要作用。 下圖說明了各種方法的分類法，包括用于情感分析技術的深度學習。 無論是通過深度學習還是傳統機器學習進行的情感分析，都要求在將文本訓練數據用于歸類之前進行清理(Dang等人，2020)。

Taxonomy of various approaches for Sentiment Analysis各種情感分析方法的分類

深度學習 (Deep Learning)

Deep Learning leverages multilayer approach to the hidden layers of neural networks. Traditionally, in machine learning models, features are identified and extracted either manually or using feature selection methods. However, in the case of Deep Learning, features are learned, extracted automatically resulting in higher accuracy and performance. Below figure shows the differences in sentiment polarity classification between the two approaches: traditional machine learning (Support Vector Machine (SVM), Bayesian networks, or decision trees) and deep learning. Artificial neural networks and deep learning currently provide the best solutions to many problems in the fields of image and speech recognition, as well as in natural language processing(Ghorbani et al., 2020). Below figure illustrates differences in sentiment polarity classification between the two approaches: traditional machine learning (Support Vector Machine (SVM), Bayesian networks, or decision trees) and deep learning techniques.

深度學習將多層方法應用于神經網絡的隱藏層。 傳統上，在機器學習模型中，特征是通過手動或使用特征選擇方法來識別和提取的。 但是，在深度學習的情況下，將學習特征并自動提取特征，從而獲得更高的準確性和性能。 下圖顯示了兩種方法在情感極性分類上的差異：傳統機器學習(支持向量機(SVM)，貝葉斯網絡或決策樹)和深度學習。 目前，人工神經網絡和深度學習為圖像和語音識別領域以及自然語言處理領域的許多問題提供了最佳解決方案(Ghorbani等，2020)。 下圖說明了兩種方法之間的情感極性分類的差異：傳統機器學習(支持向量機(SVM)，貝葉斯網絡或決策樹)和深度學習技術。

Sentiment Classification using Machine Learning and Deep Learning Techniques使用機器學習和深度學習技術進行情感分類

Key Deep Learning techniques, which can be used, are listed below –

下面列出了可以使用的關鍵深度學習技術–

Convolution Neural Networks (CNN) — It is a class of deep neural networks, most commonly used to analyze visual imagery. They are also known as space invariant or shift invariant artificial neural networks, due to shared-weights architecture and translation in-variance characteristics. CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of a series of convolution layers that convolve with a multiplication or other dot product. The activation function is commonly a RELU layer, and is subsequently followed by additional convolutions such as pooling layers, fully connected layers and normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution. Below is the deep architecture using a 10-layer convolution neural network. Starting from the inputs, this model consists of three conv-pool stages with a convolution and max-pooling each, one flatten layer, two fully-connected layers, and one softmax layer for outputs(Wang & Fey, 2018).

卷積神經網絡(CNN) -這是一類深層神經網絡，最常用于分析視覺圖像。 由于共享權重架構和翻譯不變性特征，它們也被稱為空間不變或移位不變人工神經網絡。 CNN由一個輸入層和一個輸出層以及多個隱藏層組成 。 CNN的隱藏層通常由一系列與乘法或其他點積卷積的卷積層組成。 激活函數通常是RELU層 ，隨后是其他卷積，例如池化層，完全連接的層和歸一化層，稱為隱藏層，因為它們的輸入和輸出被激活函數和最終卷積掩蓋了。 下面是使用10層卷積神經網絡的深度架構。 從輸入開始，此模型包括三個卷積級，每個卷積級和最大池級，一個平坦層，兩個完全連接層和一個用于輸出的softmax層(Wang＆Fey，2018)。

Convolution Neural Network (CNN) Architecture卷積神經網絡(CNN)架構

Deep Neural Networks (DNN) — It is an artificial neural network (ANN) with multiple layers between the input and output layers. It finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship.

深度神經網絡(DNN) -這是一種人工神經網絡(ANN)，在輸入層和輸出層之間具有多層。 無論是線性關系還是非線性關系，它都能找到正確的數學運算以將輸入轉換為輸出。

Recurrent Neural Networks — A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. The main function of RNN is the processing of sequential information on the basis of the internal memory captured by the directed cycles. Unlike traditional neural networks, RNN can remember the previous computation of information and can reuse it by applying it to the next element in the sequence of inputs. A special type of RNN is long short-term memory (LSTM), which is capable of using long memory as the input of activation functions in the hidden layer.

遞歸神經網絡 —遞歸神經網絡(RNN)是一類人工神經網絡，其中節點之間的連接沿時間序列形成有向圖。 這使其具有時間動態行為。 RNN的主要功能是基于有向循環捕獲的內部存儲器來處理順序信息。 與傳統的神經網絡不同，RNN可以記住先前的信息計算，并且可以通過將其應用于輸入序列中的下一個元素來重用它。 RNN的一種特殊類型是長短期記憶(LSTM)，它能夠將長記憶用作隱藏層中激活函數的輸入。

Long Short-Term Memory (LSTM) Architecture長短期記憶(LSTM)架構

Above figure illustrates the architecture of LSTM architecture. In this figure, input data is preprocessed to reshape the data for the embedding matrix, next layer is the LSTM and the final layer is fully connected layer for text classification(Dang et al., 2020).

上圖說明了LSTM體系結構的體系結構。 在該圖中，對輸入數據進行了預處理以對嵌入矩陣的數據進行整形，下一層是LSTM，最后一層是用于文本分類的完全連接層(Dang等，2020)。

結論 (Conclusion)

In this article, we discussed the core of deep learning models and the techniques that can be applied to sentiment analysis for social network data. We discussed about various approaches for sentiment analysis including machine learning based, lexicon based and hybrid model. The architectures of CNN, DNN and LSTM are discussed. It is better to combine deep learning techniques with word embedding when performing a sentiment analysis. Also, the effectiveness of the algorithms is largely dependent on the characteristics of the datasets, hence the convenience of testing deep learning methods with more datasets is important in order to cover a greater diversity of characteristics.

在本文中，我們討論了深度學習模型的核心以及可用于社交網絡數據情感分析的技術。 我們討論了各種情感分析方法，包括基于機器學習，基于詞典和混合模型的方法。 討論了CNN，DNN和LSTM的體系結構。 進行情感分析時，最好將深度學習技術與單詞嵌入結合起來。 而且，算法的有效性在很大程度上取決于數據集的特征，因此，為了覆蓋更大的特征多樣性，測試具有更多數據集的深度學習方法的便利性很重要。

參考書目 (Bibliography)

Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77, 236–246. https://doi.org/https://doi.org/10.1016/j.eswa.2017.02.002

Ora的Araque，I。的Corcuera-Platas，JF的Sánchez-Rada和CA的Iglesias(2017)。 在社交應用程序中使用集成技術增強深度學習情感分析。 專家系統與應用 ，77，236-246。 https://doi.org/https://doi.org/10.1016/j.eswa.2017.02.002

Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020). Sentiment Analysis Based on Deep Learning: A Comparative Study. Electronics, 9(3), 483. https://doi.org/10.3390/electronics9030483

北卡羅來納州的當當，明尼蘇達州的莫雷諾-加西亞，與德拉普萊塔(F.La 2020)。 基于深度學習的情感分析：一項比較研究。 電子學 ， 9 (3)，483. https://doi.org/10.3390/electronics9030483

Ghorbani, M., Bahaghighat, M., Xin, Q., & ?zen, F. (2020). ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing. Journal of Cloud Computing, 9(1), 16. https://doi.org/10.1186/s13677-020-00162-1

Ghorbani，M.，Bahaghighat，M.，Xin，Q.，＆?zen，F.(2020年)。 ConvLSTMConv網絡：一種用于云計算中情感分析的深度學習方法。 云計算學報 ， 9 (1)，16. https://doi.org/10.1186/s13677-020-00162-1

Goularas, D., & Kamis, S. (2019). Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), 12–17. https://doi.org/10.1109/Deep-ML.2019.00011

Goularas，D.和Kamis，S.(2019年)。 從Twitter數據進行情感分析中的深度學習技術評估。 2019年新興應用程序深度學習和機器學習國際會議(Deep-ML) ，12-17。 https://doi.org/10.1109/Deep-ML.2019.00011

Preethi, G., Krishna, P. V, Obaidat, M. S., Saritha, V., & Yenduri, S. (2017). Application of Deep Learning to Sentiment Analysis for recommender system on cloud. 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), 93–97. https://doi.org/10.1109/CITS.2017.8035341

Preethi，G.，Krishna，P.V，Obaidat，MS，Saritha，V.，＆Yenduri，S.(2017年)。 深度學習在云推薦系統情感分析中的應用 2017年計算機，信息和電信系統國際會議(CITS) ，93-97。 https://doi.org/10.1109/CITS.2017.8035341

Wang, Z., & Fey, A. M. (2018). Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International Journal of Computer Assisted Radiology and Surgery, 13, 1959–1970.

Wang，Z.，＆Fey，AM(2018)。 使用卷積神經網絡進行深度學習，以評估機器人輔助手術中的客觀技能。 國際計算機的輔助放射學和外科學 ，13，1959-1970。

Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6), 4335–4385. https://doi.org/10.1007/s10462-019-09794-5

Yadav，A.＆Vishwakarma，DK(2020)。 使用深度學習架構的情感分析：回顧。 人工智能評論 ， 53 (6)，4335-4385。 https://doi.org/10.1007/s10462-019-09794-5

翻譯自: https://medium.com/analytics-vidhya/sentiment-analysis-using-deep-learning-a416b230ca9a

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