1、卷积神经网络相关外文文献翻译中英文卷积神经网络相关外文文献翻译中英文2020英文Social media sentiment analysis through parallel dilated convolutional neural network for smart city applicationsMuhammad Alam, FazeelAbid, etcAbstractDeep Learning is considered to leverage smart cities through social media sentiment analysis. The digital con
2、tent in social media can be used for many smart city applications (SCAs). Classical convolutional neural networks (CNNs) are challenging to parallelize and insufficient to capture long term contextual semantic features for sentiment analysis. In this perspective, this paper initially proposes a doma
3、in-specific distributed word representation (DS-DWR)with a considerably small corpus size induced from textual resources in social media. In DS-DWR, different Distributed Word Representations are concatenated to builds rich representations over the input sequence, which is worthwhile for infrequent
4、and unseen terms. Second, a dilated convolutional neural network (D-CNN), which is composed of three parallel dilated convolutional neural network (PD-CNN)layers and a global average pooling (GAP)layer. Our considered parallel dilated convolution reduces dimension and incorporates an extension in th
5、e size of receptive fields without the loss of local information. Further, the long-term contextual semantic information is achieved by the use of different dilation rates. Experiments demonstrate that our architecture accomplishes comparable results with multiple hyperparameters tuning for better p
6、arallelism which leads to the minimized computational cost.Keywords：Social media sentiment analysis，Smart city applications，Parallel dilated convolutional neural network (PD-CNN)，Domain-specific distributed word representation (DS-DWR)IntroductionThe development of smart city applications (SCAs) in
7、the last decade has shown flexible design and valuable services. Smart Cities are a combination of technical and social advancements, e.g.,Internet access on Mobile, Social Data Analytics, and Internet of Things (IoT)1. These advancements can facilitate to manage resources productively primarily thr
8、ough social networks. Our study endorses a novel way for the smart cities towards the building of intelligent and adaptable SCAs. The social media has become a potential information source for social information mining to dominate peoples opinions. In our perspective, users can be viewed as social s
9、ensors, and opinions are response signals as it is real-time characteristics of social data. These opinions are extensive on social media consist of short sentences and contain useful information in numerous contexts. Therefore, social media data as social sensors can be employed to discover valuabl
10、e information. A developing pattern in the area of social data mining is to concentrate on social textual data having opinions instead of concentrating on extensive numerical analysis. Social media sentiment analysis is a growing technique to comprehend the opinions of individuals through social net
11、works. While SCAs can utilize the benefits of social media sentiment analysis as the opinions can be related to any event or a source of estimating preferences, dislikes, and patterns.SCAs can act as “activators” by focusing on improvements in technological, political, and organizational aspects thr
12、ough social media. Also, it can enhance peoples attitudes and empower them to build up a feasible environment in Smart cities2. Social networks are the primary source of opinions and events; however, the main issue is substantial social content that requires smart approaches to filter out noisy data
13、. The prerequisite of noise filtering of social media data involves automatic classification techniques for valuable facts. It also suffers a few difficulties; for example, sentences are short and contain abbreviations as well as spelling mistakes. The semantic analysis can be used to manage abundan
14、t social media data to induce semantic for improvements in terms of integration and reusability3. Similarly, social media mining techniques can utilize to collect and process social data posts on Facebook, Twitter, and Instagram for SCAs4. We choose Twitter, which has a considerable number of posts
15、and consists of less than 140 characters5. This social media data can be used for social media sentiment analysis and considered appropriate to examine peoples opinions of smart city services.Currently, Machine Learning (ML) methodologies utilized IoT and Big Data for the enhancements of smart city
16、services6. Among many ML methods on classifying text, Nave Bayes (NB) is utilized for spam detection7, topic detection8, sentiment analysis9, recommendation systems10. In a social context, popular techniques support vector machine (SVM) has been utilized to characterize tweets to get the trends11. H
17、owever, there are some issues related to the extraction of the features with variable-size sequences composition. Whereas a subfield, deep Learning (DL), favorably provides intelligent services. DL employs artificial neural networks (ANNs) architectures in order to learn high-level features for soci
18、al data with multiple layers. These features are efficient enough to replace many ML methodologies, especially when addressing social media data for predictions. Further, these methodologies are progressively applied to supervised and unsupervised classification problems12,13,14.Deep learning (DL) p
19、ersuades researchers by using ANNs that empowers efficient learning of features without requiring complex feature-engineering15,16. DL methodologies work by executing the feature extraction and classification task through initiating the representation of a sequence of words via multiplying with asso
20、ciated weight matrix as a one-hot vector17. This sequence of each word is formulated by continuous vector space inputted to a neural network to process the succession of words with the assistance of various layers for predication. This processing has an impact on the training set towards increasing
21、the classification accuracy, as explained in18. Convolutional neural network (CNN) has accomplished excellent outcomes of sentence-level classification19,20. It can learn from different distributed word representations such as Word2Vec21, FastText22and GloVe23by projecting the words into lower dimen
22、sions with dense space vector. A technique of CNN to extract the features of the sentence and joined these features with handcraft-features for the relation classification proposed in24. However, traditional CNNs are unable to hold long term semantic features. Comparatively, a unique design of CNN i
23、s the utilization of dilated convolution. It eliminates the implications of information loss, which is due to conventional down-sampling approaches in traditional pooling operations and likewise in the stride convolution. Further, it can expand the size of receptive fields at the exponential level w
24、ithout concerning additional parameters. Thus, it becomes feasible for the dilated convolution to capture long term semantic information.Although there are numerous SCAs, still it appears as a collection of loosely coupled relation with social media, which is need to be strengthened. The current res
25、earch is utilizing a deep learning methodology that automatically identifies information on social sensors by considering a parallel dilated convolutional neural Network (PD-CNN). From the best of our review, PD-CNN is capable of predicting appropriate information as it learns features from DS-DWRs
26、using different distributed word representations, as mentioned above. Beyond the improvement in SCAs, several favorable hyper-parameters while utilizing DL for social media sentiment analysis are considered. Our work not only introduces the new way of social media sentiment analysis but also useful
27、to modernize smart cities. The following are the contributions of this work:The generation of domain-specific distributed word representation is explored to discover the features for the enhanced exhibition of the classifier in comparison with standard methods.The parallelism in the dilated convolut
28、ional neural network contains three dilated convolution layers containing different dilation rates with global average pooling in a novel manner.Lastly, without the complex feature-engineering, the work proceeds with the employment of the DL method to provide intelligent Smart city applications thro
29、ugh social media sentiment analysis based on the social sensor (Tweets) to augment the peoples perception in smart cities.Related workSmart City and social media analytics continuous growth contributed to an advanced level in technological and urban advancements. Only a few studies in the last years
30、 clarify the complementary characteristics of the social-technical ecosystem. However, their mutual significance is not yet wholly idealized25. The works on sustainability which explain the smart cities by relating innovations to social networks explained in26,27. Social users of smart city services
31、 are well informed about the availability and productivity of these services, as described in28. It Implies the requirement for more practicality in smart cites research regarding social networks. For the sustainability in smart cities research, one has to concentrate on the social context related t
32、o smart cities29. In this Way, smart city applications (SCAs) prompt the integration of social networks and smart cities services having opinions and prospects, as explained in30,31. Thus, social networks have a profound impact on SCAs. Social networks contain informal methods of interactions and opinions. We attempt to analyze how these opinions contribute to promote the mentioned services. Further, these networks allowing to share status update messages. These messages have additional (meta) information, e.g.,name, time, location, and hashtags. It
