Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification

Figures & data

Figure 1. Architecture of the proposed system.

Table 1. Experimental results with Positive/Negative sentiment-value corpus.

Analysis Method	Quantity Identification	Positive Sentiment Value			Negative Sentiment Value
		Accuracy (%)	Precision (%)	Recall (%)	Accuracy (%)	Precision (%)	Recall (%)
CNN+Word2Vec	869	76.54	73.27	79.03	85.41	76.85	80.68
Naïve Bayes + SVM	456	78.76	74.38	80.51	87.07	77.59	81.67
CNN+Emoji2Word+ Word2Vec)	765	82.13	76.065	82.75	89.60	78.71	83.17
CNN+Emoji2Vec+ Word2Vec)	789	83.24	76.62	83.49	90.43	79.08	83.66
LSTM+SVM	983	85.32	77.66	84.88	91.99	79.77	84.59
RNN+SVM	921	88.12	79.06	86.75	94.09	80.71	85.83
HSVM+EVSM	887	94.23	82.12	90.82	98.67	82.74	88.55

Figure 2. Tuning the rate.

Figure 3. Tuning the rate.

Table 2. Experimental results for negative and positive sentiments.

Methods	Positive Rate	Negative rate
Random Forest	88.5	93.7
Deep neural Networks	87.5	92.8
RNN	78.4	90.77
SVM	7.33	96.4
Logistic Regression	72.1	93.6
K-Nearest Neighbour	89.0	96.67
Proposed Method	91.33	97.32

Figure 4. Graph representing the comparison of positive and negative sentiment rates.

Table 3. F scores for the training sets and testing sets of twitter.

Features	F Score
	Twitter Test	Twitter Train
All Features	64.54	80.12
All lexicons	60.12	79.64
All automatic	64.25	82.18
All Manual	65.26	76.49
All Sentiments	66.75	83.26
All Sent WordNet	72.54	89.18
All Sentiment	68.74	92.22
Proposed System	76.54	94.12

Figure 5. Performance measures of various methods against different performance indicators.