Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)¹ . This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)² . Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)³ to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11% on SST.

文本中的主观性检测对于情感分析是必不可少的，情感分析需要许多技术来感知意想不到的交流方式。很少有成就能够通过分布式单词表示（DWR）¹来捕获语法，语义和上下文情感信息。本文通过加权神经网络（RNN）变体联合卷积神经网络（CNN）的加权机制，将DWR串联在一起，这特别涉及加权注意力集中（WAP）²。相比之下，具有传统合并操作的CNN包含许多仅能捕获足够特征的层。我们的考虑使基于DWR的情感分析（包括Word2vec，FastText和GloVe）能够产生密集的有效级联表示（DECR）³保留对单个RNN层的长期依赖关系，该RNN层由语音标记（POS）明确地仅使用动词，副词和名词获得。然后使用以某种方式获得的这些表示形式，输入到CNN中，包含在多源社交媒体数据上使用WAP的单个卷积层，以处理语法和语义规则性以及词汇量（OOV）单词的问题。实验表明，DWR和建议的串联可以通过中等超参数配置解决上述问题。与IMST上的随机初始化为81.11％相比，IMDB上的DECR-Bi-GRU-CNN（WAP）避免了多层堆叠的体系结构实现了89.67％的适度精度。