Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Abstract

Twitter sentiment analysis provides valuable feedback from public emotion concerning certain events or products. Current research has been focused on obtaining sentiment features from vectorized lexical and syntactic feature from tweets, without further context. In this paper, we demonstrated how vectorized location information could be combined with word embeddings to produce a hybrid representation, which has resulted in an improvement on a tweet sentiment classification task. The location information of the geo-tagged tweets provided further context, which was useful for a sentiment classification task. The tweets investigated contained a set of geo-tagged tweets. The word embeddings of these tweets were combined with the geo-tagged tweets' vectorized location features to form a sentiment feature set of geo-tagged tweets. The sentiment feature set was incorporated into a convolution neural network and a bi-directional long short-term memory network for the tasks of training and predicting of sentiment classification labels. This hybrid representation is compared with the baseline GloVe model through a few experiments, and the results have shown that the incorporation of vectorized location information has resulted in improvement of the accuracy for the twitter sentiment classification task.

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

“…Word2Vec: Word2vec [11,14,38,44,53,56,65] a neural network model will be used to learn associations of words from a large corpus of text. A model can detect synonymous words or suggest additional words for a partial sentence, once trained.…”

“…GloVe: GloVe [11,14,[41][42][43]53], a unsupervised learning algorithm by mapping words into a meaningful space for obtaining words vector representations where the semantic similarity is related to the distance between words.…”

“…Mokhlesur Rahmana [100] Bag of Words (BOW) Ganzalo A. Ruz [4], Li Chen Cheng [11], Md. Mokhlesur Rahmana [100] Term Document Matrix Micheal T. Tadasse [16], Kashif Ayyab [43], Maruo Dang [46], Jayakrishnan [82] N Gram Kashif Ayyab [43] TD-IDF + N Grams Fazeel Abid [11], Hudan Studiawan [41], Mohd Usama [42], Kashif Ayyab [43], Wei Lun Lin [53] GloVe Micheal T. Tadasse [16], Md. Rafiqul Islam [24], Xingwei Yang [5], Dilesh Tama [7] LIWC Fazeel Abid [11], Mehmet Umut Salur [38], Ali Feizollah [44], Wei Lun Lin [53], Irum Sindhu [56], Da Li [65], Hay Mar Su Aung [74], Shan Huang [90] word2vec Badr Ait Hamou [15], Mehmet Umut Salur [38] FastText Ali Feizollah [44] word2seq L. Ming Dang [49] stock2vec Wei Lun Lin [53] Static Word Embedding which learns more accurate in its predictions [2].…”

“…In Manoj Sethi et al [2], Twitter is the data source used mainly to gather tweets specific to the corona virus. 3 datasets were used totally, Ttweets with hashtag #coronavirus -Dataset 1, Table 9 Data sources Papers Data Source Manoj Sethi [1], Gonzalo A. Ruz [4], Xiaohui Tao [6], Priyanka Arora [8],Abdul Hasib Uddin [10], Nafiz Al Asad [12], Fazeel Abid [14], Badr Ait Hammou [15], Jwan Syaruf [20], Guangyao Shen [25],Akshi Kumar [26],Shakeel Ahmed [30], Mohammed Jabreel [32], Fuhai Chen [37], Mehmet Umut Salur [38], Kashif Ayyab [43], Ali Feizollah [44], Wei Lun Lim [53], Le Hoang Son [55], Rywan Khan [58],Priti Sharma [60], Fang Yao [61],Yingwei Yan [62],Samah Maneoue [64], Veny Amilia Fitri [66],Marouane Biyali [68], Jonathan G. D. Haib [70], Shenghio Liu [71], Mondher Bouazizi [75], Govin Gaikwad [79],Marija Stanojevic [81],…”

Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine

“…Geo-tag [14] based implementation have been helpful for the researchers to locate the user who is tweeting on a specific topic. Geo-tag reflects the motto of the tweet from a specific user.…”