scholarly journals The Impact of Figurative Language on Sentiment Analysis

Author(s):  
Tomáš Hercig ◽  
◽  
Ladislav Lenc
2021 ◽  
Vol 184 ◽  
pp. 148-155
Author(s):  
Abdul Munem Nerabie ◽  
Manar AlKhatib ◽  
Sujith Samuel Mathew ◽  
May El Barachi ◽  
Farhad Oroumchian

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 374 ◽  
Author(s):  
Sudhanshu Kumar ◽  
Monika Gahalawat ◽  
Partha Pratim Roy ◽  
Debi Prosad Dogra ◽  
Byung-Gyu Kim

Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.


2017 ◽  
Vol 24 (2) ◽  
pp. 163-186
Author(s):  
R. V. Young

Although T.S. Eliot's phrase “dissociation of sensibility,” applied to the changes in poetry during the seventeenth century, made a stir when he introduced it in the review essay “The Metaphysical Poets” in 1921, it draws less attention now, and seems never to have been adequately explained. Since Eliot's claims are, in part, historical, it makes sense to consider the most historically significant changes occurring during the seventeenth century. It is during this period that the Reformation culminates and its effects become permanently established. Several recent studies of the Reformation by Charles Taylor, Brad Gregory, and Carlos M.N. Eire provide clues about how the religious and social cataclysm of the sixteenth and seventeenth centuries may have affected the poetic imagination. James Smith's classic essay, “The Metaphysical Poets,” offers a way of analyzing the figurative language of seventeenth-century poetry in order to grasp the impact of the religious change. The investigations by Taylor, Gregory, and Eire into the dynamic of the reforming tendency, beginning in the late Middle Ages, as well as the Scotist and nominalist intellectual underpinnings of the Reformation, prove to be pertinent to Eliot's insight regarding seventeenth-century poetry. The growth of individualism, personal anxiety about religious choice, and materialism portend a general movement towards secularization and influence the way poets see the world. Dissociation of sensibility can thus be understood as a result of the effect of the religious and social dislocations of the Reformation in the realm of poetry.


2015 ◽  
Author(s):  
Hoang Long Nguyen ◽  
Trung Duc Nguyen ◽  
Dosam Hwang ◽  
Jason J. Jung

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.


Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 280
Author(s):  
Shaoxiu Wang ◽  
Yonghua Zhu ◽  
Wenjing Gao ◽  
Meng Cao ◽  
Mengyao Li

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.


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