Practical Signal Processing and Its Applications

Laxpati, S.; Goncharoff, Vladimir

doi:10.1142/10551

Cited by 5 publications

(4 citation statements)

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“…While convenient as notation, this makes evident two limitations at this point. First, point process s(t, f ) is hard to analyze in this form, as most of Stochastic-Process Theory works on temporal evolution according to a sample period T s providing a regularly-sampled discrete-time series from the continuous time original series [38]. The second limitation, directly due to the sampling process, is that populational information is aggregated and heavily condensed, hence mixing different basic properties of the underlying multidimensional sentiment process in a single measurement.…”

Section: B Time Signals M-mode and Autocorrelationmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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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.

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Section: B Time Signals M-mode and Autocorrelationmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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“…This is a question to which we plan to find an answer in this paper. This answer will be based on the general engineering signal processing ideas; see, e.g., [2,3]. Moreover, not only we explain why this works, we also show that such filtering is the optimal way to make it easier for a person to carry a tune.…”

Section: Formulation Of the Problemmentioning

confidence: 83%

Why filtering out higher harmonics makes it easier to carry a tune

Acosta¹,

Freudenthal²,

Smith³

et al. 2019

ams

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“…This is a question to which we plan to find an answer in this chapter. This answer will be based on the general engineering signal processing ideas; see, e.g., [74,77]. Moreover, not only we explain why this works, we also show that such filtering is the optimal way to make it easier for a person to carry a tune.…”

Section: Formulation Of the Problemmentioning

confidence: 85%

Towards Analytical Techniques for Systems Engineering Applications

Smith¹,

Kreinovich²,

Acosta³

2020

Studies in Systems, Decision and Control

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Formulation of the Problem Main objectives of systems engineering: a brief reminder. One of the main objectives of systems engineering is to design, maintain, and analyze systems that help the users. To design an appropriate system for an application domain, we need to know: • what are the users' desires and preferences, so that we know in what direction we should aim to change this domain, and • what is the current state and what is the dynamics of this application domain, and • how to use all this information to select the best alternatives for the system design and maintenance. Need for analytical techniques. Designing a system includes selecting numerical values for many of the parameters describing the corresponding system and its subsystems. At present, in many cases, this selection is made by consulting experts and/or by following semi-heuristic recommendations (recommendations based partly on the past experience of system design and monitoring). Experience shows that such heuristic imprecise recommendations often lead to less-than-perfect results. It is therefore desirable to come up with analytical techniques for system design, techniques that would be based on valid numerical analysis and on the solution of the corresponding optimization problems. What we do in this dissertation: general idea. System engineering is a very broad discipline, with many different application domains. Each domain has its own specifics and requires its own analysis and, probably, it own analytical techniques. 1 What we do in this dissertation is we formulate and analyze general problems corresponding to different stages of system design, implementation, testing, and monitoring, and show, on appropriate examples, how the corresponding analytical techniques can be applied to different application domains. What we do in this dissertation: a detailed description. We start with analytical techniques for describing the users' preferences. In the ideal world, we should be able to ask each user's opinion about each of the alternatives, but for large systems, with many possible alternatives, this is not realistic. Therefore, we need to extrapolate the user's preferences based on partial information that we can elicit from the user. There are analytical techniques for such extrapolation-e.g., the widely used matrix factorization technique. However, this technique is purely empirical-and thus, not very reliable. In Chapter 2, we provide a theoretical explanation for this techniques-and the existence of such an explanation makes it more reliable. In analyzing user preferences, we need to take into account that these preferences are usually not very detailed-and thus, because of their approximate nature, we should not waste time trying to fit them optimally. This approximate nature is usually captured by the empirical 7 plus minus 2 law, according to which, in the first approximation, instead of sorting all the alternatives, a user usually divides them into 7 plus minus 2 groups. This law is purely empirical-and thus, its use is not as re...

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Practical Signal Processing and Its Applications

Cited by 5 publications

References 0 publications

On the Statistical and Temporal Dynamics of Sentiment Analysis

On the Statistical and Temporal Dynamics of Sentiment Analysis

Why filtering out higher harmonics makes it easier to carry a tune

Towards Analytical Techniques for Systems Engineering Applications

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