###### Abstract  
The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For perspectivism, a social network analysis (SNA) was conducted using popular SNA tools (e.g., Graphisty). An influence weighting of users was performed using an artificial neural network (ANN) based on the SNA provided output and people's reactions to the OM analyzed texts. The initiative adoption of neutrosophic logic (NL) to integrate users' influence with their OM scores is to deal with both the opinion dynamicity and indeterminacy. Thus, it provides new uncertainty OM scores that can reflect everyone. The OM scores needed for integration were generated using TextBlob. The results show the ability of NL to improve the OM process and accurately consider the innumerable degrees. This will eventually aid in a better understanding of people's opinions, helping OM in social media to become a real pillar of many applications, especially business marketing. social network analysis; artificial neural networks; neutrosophic logic; opinion mining +
Footnote †: journal: _Applied_  
spread positive opinions of their business [7]. Using the graph and network theory, SNA is one way to study the importance of users in their social networks. The centrality measure in SNA is one primary assessment for detecting influential nodes (i.e., users) in a given graph network [4]. However, this sort of action can increase the diffusion of positive opinions with no effect on the main process; OM deals with opinion texts only [1]. The subjective nature of opinions makes them subject to many types of uncertainty when being automatically analyzed. For example, opinions have gradient polarity; two positive texts are not positive to the same degree. Moreover, undecided polarity can happen when text polarity is at an equal distance between two polarity classes. At that instant, the machine makes the approximate or dominant choice (e.g., neutral class). Likewise, many neutral classified opinions are undecided (e.g., having the same opposite polarity) but such a class is not considered in OM classification [8]. These innumerable degrees reflect reality and are found in social media texts, which then need to be considered for more accurate and real OM results. More than that, when individuals perform text rating, this may cause the generation of different rates for the same texts based on the observers' perspective. Opinion dynamics on OSNs is one possible result of perspectivism, thus correlated with SNA in the determinacy of users' importance and trust degree [7; 9; 10]. All the above-mentioned cases can be easily detected and solved by humans, but hardly processed by machines [4]. Leaving aside its unfamiliar implementation in OM, the nature of neutrosophic logic (NL) is highly integrated with how humans think, where an indefinite environment is a human's ordinary space to draw a conclusion or make a judgment due to incomplete knowledge. Furthermore, NL is malleable in terms of accepting different values from observers. Thus, NL can acquire some mental ability for the OM process to become smart. In NL, each object in a certain universe has a degree of truth (\(T\)), a degree of indeterminacy (\(I\)), and a degree of falsity (\(F\)), where \(T,\ I,\ F\) are standard or non-standard real subsets of \(\left]-0,1^{+}\right[\). Such a classification might help in determining the percent of indeterminacy in a text's polarity [9].