Predicting the sentiment in sentences based on words: An Exploratory Study on ANEW and ANET

Date

2012-12-05

Author

Gökçay, Didem
Isbilir, E.
Yildirim, G.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

146
views

0
downloads

Current practices for sentiment prediction from text mostly involve words-in-a-bag approach that utilize techniques such as support vector machines or naive Bayes. In this study, ANET (Affective Norms for English Text) sentence ratings of pleasure and arousal are compared with ANEW (Affective Norms for English Words) word ratings using regression and single layer neural networks. The sentences in ANET are decomposed into their words to obtain valence and arousal ratings from ANEW. A stop list is formed for non-words as well as words that are not found in ANEW. Then we studied whether the sentence sentiment reflected in terms of valence and arousal can be predicted from the sentiment of words in the sentence. Using linear regression, we found that approximately 35% of the variance in ANET valence and arousal ratings can be explained by ANEW valence and arousal ratings. Furthermore, Pearson correlation coefficient for ANEW and ANET ratings are similar for both valence and arousal, and close to 0.6. We also trained neural networks to investigate if non-linear approximations improved prediction of sentence sentiments from the constituent words. Out of several feedforward neural network configurations, a network with 200 hidden layer nodes turned out to be capable of identifying sentence sentiments accurately: the words' valence and arousal values explained 88 % of the variance in the sentences' valence ratings and 91 % of the variance in the sentences' arousal ratings. This preliminary study indicates that a proper choice of neural network might be adequate to estimate sentiments of sentences from sentiments of words.

URI

https://hdl.handle.net/11511/53587

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Improving Perceptual Quality of Spatially Transformed Adversarial Examples Aydın, Ayberk; Temizel, Alptekin; Department of Modeling and Simulation (2022-8) Deep neural networks are known to be vulnerable to additive adversarial perturbations. The amount of these additive perturbations are generally quantified using Lp metrics over the difference between adversarial and benign examples. However, even when the measured perturbations are small, they tend to be noticeable by human observers since Lp distance metrics are not representative of human perception. Spatially transformed examples work by distorting pixel locations instead of applying an additive perturba...
Extracting Sequential Patterns Based on User Defined Criteria Alkan, Oznur Kirmemis; Karagöz, Pınar (2013-09-13) Sequential pattern extraction is essential in many applications like bioinformatics and consumer behavior analysis. Various frequent sequential pattern mining algorithms have been developed that mine the set of frequent subsequences satisfying a minimum support constraint in a transaction database. In this paper, a hybrid framework to sequential pattern mining problem is proposed which combines clustering together with a novel pattern extraction algorithm that is based on an evaluation function, which utili...
Reducing Features to Improve Link Prediction Performance in Location Based Social Networks, Non-Monotonically Selected Subset from Feature Clusters Bayrak, Ahmet Engin; Polat, Faruk (2019-01-01) In most cases, feature sets available for machine learning algorithms require a feature engineering approach to pick the subset for optimal performance. During our link prediction research, we had observed the same challenge for features of Location Based Social Networks (LBSNs). We applied multiple reduction approaches to avoid performance issues caused by redundancy and relevance interactions between features. One of the approaches was the custom two-step method; starts with clustering features based on t...
Numerical study on effects of computational domain length on flow field in standing wave thermoacoustic couple MERGEN, SÜHAN; Yıldırım, Ender; TÜRKOĞLU, HAŞMET (Elsevier BV, 2019-03-01) For the analysis of thermoacoustic (TA) devices, computational methods are commonly used. In the computational studies found in the literature, the flow domain has been modelled differently by different researchers. A common approach in modelling the flow domain is to truncate the computational domain around the stack, instead of modelling the whole resonator to save computational time. However, where to truncate the domain is not clear. In this study, we have investigated how the simulation results are aff...
Encoding the local connectivity patterns of fMRI for cognitive task and state classification Ertugrul, Itir Onal; Ozay, Mete; Yarman Vural, Fatoş Tunay (Springer Science and Business Media LLC, 2019-08-01) In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussia...

Citation Formats

D. Gökçay, E. Isbilir, and G. Yildirim, “Predicting the sentiment in sentences based on words: An Exploratory Study on ANEW and ANET,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53587.