Previous work has recognized the importance of using the attention mechanism to obtain the interaction between aspect words and contexts for sentiment analysis. However, for the most attention mechanisms, it is unrigorous to use the average vector of the aspect words to calculate the context attention. Besides, the feature extraction ability of the model is also essential for effective analysis, the combination of CNN and LSTM can enhance the feature extraction ability and semantic expression ability of the model, which is also a popular research trend. This paper introduces an aspect level neural network for sentiment analysis named Feature Enhanced Attention CNN-BiLSTM (FEA-NN). Our method is to extract a higherlevel phrase representation sequence from the embedding layer by using CNN, which provides effective support for subsequent coding tasks. In order to improve the quality of context encoding and preserve semantic information, we use BiLSTM to capture both local features of phrases as well as global and temporal sentence semantics. Besides, we add an attention mechanism to model interaction relationships between aspect words and sentences to focus on those keywords of targets to learn more effective context representation. We evaluate the proposed model on three datasets: Restaurant, Laptop, and Twitter. Extensive experiments show that the effectivess of FEA-NN. INDEX TERMS Aspect-based sentiment analysis, BiLSTM, CNN, attention mechanism.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">There are many characteristics in high-technology project investment, such as needing a lot of capital, having so many unsure factors and so on. There are full of risks in the investment process. Some corporations were disastrous in investment because of ignoring the risk evaluation or using the inapposite valuation methods. So it is necessary to use a scientific evaluation method in the investment of high-technology. This paper attempts to structure a risk evaluation model of high-technology project</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"> <span class="text">investment based on the uncertain linguistic variable and TOPSIS method. Firstly, this paper discusses the current situation of the research of the high-technology project investment. Then a evaluation</span> <span class="text">indicators system is constructed and the evaluation procedures based on the uncertain linguistic and TOPSIS method. At last, this evaluation method is used to a practical example.</span></span></p>
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