On the Statistical and Temporal Dynamics of Sentiment Analysis

Abstract

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

“…In this work, we propose the use of window aggregation in the time domain to account for timeevenly distributed samples, and by doing so, we can define an appropriate environment for analyzing sentiment as a discrete-time process. The statistical analysis developed here, consistent with that in previous works [3], incorporates the detailed study of the variables intensity, feeling, their mean and standard deviation, as well as the calculation of entropy and mutual information from these variables. Some of them are summarized below for reader's convenience.…”

“…Machine learning techniques are used to create models that predict sentiment from text using classification algorithms [3]. Approaches based on machine learning can use supervised or unsupervised learning techniques to build a model from training data.…”

“…media environments. In the case of Twitter,approximately two hundred papers are published annually that include the keywords sentiment and twitter in their titles [1]- [3]. One reason for this large amount of research activity in the area of social media is the cost-effectiveness of gathering large datasets for scrutinizing the information conveyed by its different platforms.…”

“…For this purpose, tweets related to the main political leaders and their parties were collected starting a month before and up to one week after the presidential elections in Spain. Building on models developed and validated in previous works [3], we then performed an in-depth analysis of the statistical properties of these data. We started by consolidating and validating various lexicons on which we based our sentiment extraction process, as this is fundamental for any subsequent analysis.…”

“…Then, a twofold strategy was followed: first, we selected some indices mostly based on information theory, which a priori could allow us to retrieve informative knowledge about the dynamics and statistical evolution of the sentiment. In this setting, variables such as sample entropy, mutual information, and a recently proposed positivity index [3] were scrutinized, and several experiments were performed to explore the dynamics of these variables throughout the election period. Second, we aimed to analyze in detail the information retrieved by intrinsic nonlinear mappings of input spaces to embedded spaces, as this represents an intrinsic and likely nonlinear feature extraction.…”