The traditional attention-based neural network model can not effectively pay attention to aspect features and sentiment information, and context words of different distances or different directions have different contributions to the sentiment polarity assessment of aspect words. Aiming at these problems, Location Weight and Attention-Over-Attention Long-short Term Memory (LWAOA-LSTM) model was proposed. Firstly, the location weight information was added to the word vectors. Then Long-Short Term Memory (LSTM) network was used to simultaneously model aspects and sentences to generate aspect representation and sentence representation, and the aspect and sentence representations were learned simultaneously through attention-over-attention module to obtain the interactions from the aspect to the text and from the text to the aspect, and the important part of the sentence was automatically paid attention to. Finally, the experiments were carried out on different thematic datasets of attractions, catering and accommodation, and the accuracy of the aspect level sentiment analysis by the model was verified. Experimental results show that the accuracy of the model on the datasets of attractions, catering and accommodation is 78.3%, 80.6% and 82.1% respectively, and LWAOA-LSTM has better performance than traditional LSTM network model.

Aiming at the low accuracy caused by the phenomenon of rule weight weakening from iterations of machine learning when judging the sentence relationships by applying rules and machine learning methods, the method of strengthening the imported obvious rule characteristics in the process of combining rules and machine learning was proposed. Firstly, these specific characteristics that having obvious rules such as dependency vocabulary, syntax and semantics information were extracted; secondly, universal characteristics were extracted based on these words that could indicate relationships; then, the characteristics were written into the data vector that to be input, and another dimensional vector was added to store the obvious rule characteristics; Finally, rules and machine learning methods were combined with LIBSVM model to perform the experiment. The experimental results show that the accuracy rate is averagely 2% higher than that before strengthening the characteristics, and all kinds of relationships' accurate rate, recall rate and F1 value show good results as a whole, their average values achieved 82.02%, 88.95% and 84.76%. The experimental ideas and methods are important for studying the compactness of adjacent sentences.

To overcome the shortcomings of conventional algal bloom forecast system in acquiring data, this study applied the Internet of Things (IoT) technology to establish a data transmission network with three-layer structure, and thus secured data continuity. With improved retrieval approach of water quality parameters, technology of Wireless Sensor Network (WSN) and forecast model of algal bloom, the blue-green algal bloom forecast platform was developed. The evaluation demonstrates that the platform achieves an overall accuracy of 80% in forecasting blue-green blooms in Taihu Lake in next three days.