While Natural Language Processing (NLP) techniques can be used to identify sentiment in text, information sources such as the neu- ral signals of a reader are typically not incorporated into the process. In this paper, we investigated whether measures extracted from Electroen- cephalography (EEG) signals during reading could be used to identify the sentiment of sentences. Our study used the ZuCo dataset which con- tained 18 channels of EEG collected from 10 native English speakers as they read 400 sentences. Each sentence belonged to a positive, negative or neutral sentiment class. We show how Detrended Fluctuation Analy- sis (DFA), an extension to chaotic systems fluctuation analysis, can be used to identify differences and changes in human EEG for reading texts with different sentiments. Based on DFA, on each time scale, we found that the left and right occipital electrodes had the greatest activation between sentiment conditions, and the EEG at electrodes over temporal- frontal scalp sites showed a significant change over many frequency bands for texts of different sentiment. Additionally, we also compared DFA to descriptive statistics to show that DFA is a useful technique for EEG analysis.