A Survey of the Usages of Deep Learning in Natural Language Processing <s> Summary of Deep Learning NLP Applications <s> In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams. <s> BIB001 </s> A Survey of the Usages of Deep Learning in Natural Language Processing <s> Summary of Deep Learning NLP Applications <s> Conventional quantitative methods for the measurement of organizational legitimacy consider mainly three sources that make judgments about organizations visible: news media, accreditation bodies, and surveys. Over the last decade, however, social media have enabled ordinary citizens to bypass the gatekeeping function of these institutional evaluators and autonomously make individual judgments public. This inclusion of voices beyond functional and formally organized stakeholder groups potentially pluralizes the ongoing discussions about organizations. The individual judgments in blogs, tweets, and Facebook posts give indication about the broader fit between an organization’s perceived behavior and heterogeneous social norms and therefore constitute an indicator of organizational legitimacy that can be accessed and measured. We propose the use of social media data and sentiment analysis to study the affect-based responses to organizational actions by citizens. We critically discuss and compare the method wi... <s> BIB002 </s> A Survey of the Usages of Deep Learning in Natural Language Processing <s> Summary of Deep Learning NLP Applications <s> With the growing availability and popularity of online reviews, the sentiment analysis arises in response to the requirement of organizing useful information in speed. Feature selection directly affects the representation of online reviews and brings a lot of challenges to the domain of sentiment analysis. However, little attention has been paid to feature selection of Chinese online reviews so far. Therefore, we are motivated to explore the effects of feature selection on sentiment analysis of Chinese online reviews. Firstly, N-char-grams and N-POS-grams are selected as the potential sentimental features. Then, the improved Document Frequency method is used to select feature subsets, and the Boolean Weighting method is adopted to calculate feature weight. At last, experiments based on online reviews of mobile phone are conducted, and Chi-square test is carried out to test the significance of experimental results. The results suggest that sentiment analysis of Chinese online reviews obtains higher accuracy when taking 4-POS-grams as features. Besides that, low order N-char-grams can achieve a better performance than high order N-char-grams when taking N-char-grams as features. Furthermore, the improved document frequency achieves significant improvement in sentiment analysis of Chinese online reviews. <s> BIB003
Numerous other applications of natural language processing exist including grammar correction, as seen in word processors, and author mimicking, which, given sufficient data, generates text replicating the style of a particular writer. Many of these applications are infrequently used, understudied, or not yet exposed to deep learning. However, the area of sentiment analysis should be noted, as it is becoming increasingly popular and utilizing deep learning. In large part a semantic task, it is the extraction of a writer's sentiment-their positive, negative, or neutral inclination towards some subject or idea  . Applications are varied, including product research, futures prediction, social media analysis, and classification of spam BIB002 BIB003 . The current state of the art uses an ensemble including both LSTMs and CNNs BIB001 . This section has provided a number of select examples of the applied usages of deep learning in natural language processing. Countless studies have been conducted in these and similar areas, chronicling the ways in which deep learning has facilitated the successful use of natural language in a wide variety of applications. Only a minuscule fraction of such work has been referred to in this survey. Initial work in many application domains often comes from academia, but then the ideas are frequently adopted, as well as expanded upon, in industry by organizations big and small, established and startup. Published academic work, as well as commercial work, is likely to continue apace, if not faster, in the years to come.