# AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews
## 2 Literature Review  
The computational study of people's views, feelings, emotions, assessments, and attitudes toward entities including goods, services, organizations, issues, topics, events, and their attributes is known as sentiment analysis or opinion mining [11]. The term sentiment analysis was proposed by [PERSON] and [PERSON] [12], but the task of sentiment analysis was first proposed by [PERSON] and [PERSON] [13]. Since the early 2000s, sentiment analysis has developed into one of the most active study topics in NLP [14]. Text sentiment analysis research is now being studied in a wide range of domains, including NLP, information extraction, information retrieval, text mining, ontology, and machine learning. It has become one of the top concerns in the field of NLPduring the past few years, with the discussed methods including the lexicon-based method and the machine learning method. The former is an unsupervised classification method, relying on the sentiment lexicon and a sentiment polarity score to achieve the calculation of sentence sentiment score to recognize emotional tendencies. [PERSON] et al. [15] proposed a method of building and expending a computer terminology dictionary based on the semantic dictionary WordNet. The advantage of this method is that the semantic structure of WordNet was preserved, and the automatic extension was constructed without manual intervention, which greatly reduced the workload. An approach to building a domain emotion dictionary was put out by [PERSON] et al. [16], which included mutual information to assign terms with part-of-speech (POS) tags in the lexicon. The method significantly improved the recognition performance of negative reviews. To help in sentiment analysis of social media content, [PERSON] et al. [17] created the first sentiment dictionary of slang words, named 'SlangSD'. 'SlangSD' can easily be utilized as an extra emotion lexicon and can effectively improve the performance of even the most sophisticated casual text. [PERSON] et al. [18] suggested a hybrid approach for training a semi-manual sentiment dictionary. This method solved the heterogeneous effect of multiple intensifiers, increased the effectiveness of existing dictionaries in document polarity recognition, and took the relationship between emotion words and multiple intensifiers into account. [PERSON] et al. [19] investigated changes in Chinese public firms' attitudes toward environmental protection between 2018 and 2021 by using a sentiment analysis method based on a sentiment dictionary. The study examined the relationship between firms' carbon reduction attitudes and financial performance, finding that specific policies can increase positive attitudes toward carbon reduction, and attitudes toward ecological topics differ across industries. Although prior knowledge has a certain reference value and significance for the methods based on sentiment dictionaries, it is dependent on the domain of the corpus, which is easily affected by the sentence structure of the text in the corpus, and the classification effect depends on the quality of the sentiment dictionary. This leads to instability in the sentiment analysis method, and the sentiment dictionary consumes huge resources for long-term maintenance. With the rapid development of modern networks, network data are growing at an unprecedented rate, so new words and expressions emerge one after another, and more and more old words with new meanings emerge, which makes it very difficult to maintain the sentiment dictionary to keep up with the pace of network updates. To solve the shortcomings mentioned above, scholars have proposed a series of sentiment analysis methods based on machine learning algorithms, including classical machine learning methods and deep learning methods. The method based on deep learning has more advantages than the lexicon-based method mentioned above. Firstly, it achieves the goal of sentiment analysis by designing an algorithm training model.