A sentence-level sentiment analysis of some literary texts inEnglish language

We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence. By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text.我们针对英语文学文本开展了句子级情感分析研究。每段文本均被转换为一组序列，其中每个数据点对应单句的情感值。通过对这些情感序列应用去趋势波动分析（Detrended Fluctuation Analysis, DFA）与Higuchi分形维数（Higuchi Fractal Dimension, HFD）方法，我们发现其属于具备长期相关性的单分形结构，这一结论可通过写作过程本质上带有记忆属性、情感演化具备自相似性来解释。此外，我们采用分类方法对这些序列进行离散化处理，将每个数据点映射为三种仅有的可能取值，分别对应积极、中性与消极情感。我们将此类三状态序列构建为马尔可夫链（Markov chain），并探究句间的情感转移规律，最终为每部书籍生成对应的状态转移矩阵，该矩阵可反映句间情感转移的概率分布。该分析结果表明，句间情感向中性或积极语句转移的概率存在偏倚。两种分析方法互为补充：长期相关性分析方法可实现对整部书籍情感的全局评估，而状态转移矩阵方法则能够提供文本沿句演进的局部情感演化信息。