Now this works fine for me, but I am just wondering, could you also create bullets when using \subitem instead of a nested list, like below? I do get an indentation at my subquestions, but no bullets.
I am having trouble making the subtitle for my ggplot2 graph. I tried the answer here but it is only applicable if you have one scientific name. In my case, I have two scientific names that I wanted to include in the plot title.
400 Bullets Subtitle Download
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The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some traditional affective computing methods only consider the text information and ignore the information of other dimensions, we construct a bullet subtitle affective computing method by combining the information of other dimensions of the bullet subtitle. Finally, aiming at the problem that existing classification algorithms ignore the importance of sentiment words in short texts, we propose a sentiment classification method based on affective computing and ensemble learning. Our experiment results show that the proposed method has higher accuracy and better practical application effect.
In recent years, the bullet subtitle video site has become more and more popular among young people, and Bilibili is one of the typical representatives. The bullet subtitle text has the characteristics of traditional short text such as short text length and sparse features, but it is different from traditional short text at the same time. First, the bullet subtitle text is more simplified, colloquially, and symbolized; second, it often contains many teasing contents; third, it also contains conversational properties. Analyzing the sentiment of the bullet subtitle text, and then analyzing the overall sentiment distribution of the bullet subtitle video, can be used as a reference basis for website operators to make personalized video recommendations to users and can also provide decision support for public opinion governance in cyberspace security.
In the sentiment lexicon-based classification method, the sentiment lexicon used to calculate the sentiment value of the text plays a key role. Currently, English sentiment lexicons mainly use Word-Net-Affect [2] and Senti-WordNet [3], and Chinese sentiment lexicons mainly use HowNet Chinese Sentiment Lexicon and the Sentiment Vocabulary Ontology Library proposed by the Dalian University of Technology [4]. Based on the English sentiment lexicon, Taboada et al. [5] proposed a semantic-oriented calculation method SO-CAL, which is based on the lexicon to classify the sentiment polarity of the text. Based on the Chinese sentiment lexicon, Zhang et al. [6] realized the sentiment analysis of Chinese microblog text by extending the sentiment lexicon. Yao et al. [7] added Weibo emoji to the sentiment lexicon, which improved the result of sentiment judgment on Weibo containing emoji. Li et al. [8] proposed a seven-dimensional affective computing method for the bullet subtitle, which is relatively simple and only uses text information. The sentiment lexicon-based classification method performs well for text data containing sentiment words, but cannot handle text data without sentiment words and has insufficient generalization ability.
The contributions of this paper mainly include first, by analyzing the bullet subtitle text data and collecting buzzwords on the Internet in recent years, we obtain the buzzword lexicon and the emoji lexicon, and finally, they are combined with the Sentiment Vocabulary Ontology Library to construct the Chinese Bullet Subtitle Sentiment Lexicon. Second, using the multiple dimensional information of the bullet subtitle, gain intensity is constructed, and combined with the text affective computing method, we propose Bullet Subtitle Calculation (BS-CAL), a multidimensional sentiment value calculation method for bullet subtitle. Finally, based on the analysis of existing algorithms, we propose a sentiment classification method for the bullet subtitle text, and comparative experiments are conducted between different methods and data sets.
Currently, existing Chinese sentiment lexicons do not contain buzzwords in bullet subtitle texts, which will lead to inaccurate affective computing or even the inability to calculate sentiment. Therefore, we crawl a large amount of bullet subtitle data from Bilibili and conduct systematic analysis, and we collect common buzzwords on the Internet, and finally fuse them to obtain the Chinese Bullet Subtitle Sentiment Lexicon. At the same time, the analysis reveals that the information of other dimensions of the bullet subtitle has some influence on the bullet subtitle sentiment, which is not taken into account by the traditional text affective computing method. Therefore, we propose BS-CAL by fusing the textual information of bullet subtitle texts and other dimensional information. Finally, the classification problem of bullet subtitle sentiment can be handled in two cases according to whether the bullet subtitle contains sentiment words: when it contains sentiment words, a heterogeneous ensemble learning method is used for prediction; when it does not contain sentiment words, the sentiment lexicon-based method cannot be used, and it is backed off to a single model method for prediction (see Figure 2).
There are a large number of special words, buzzwords, and internet phrases in the bullet subtitle text, and we obtained the bullet subtitle buzzword lexicon containing 2659 words. At the same time, the bullet subtitle text contains a large number of emojis, which contain rich information. Therefore, we collected 431 emojis as the emoji lexicon.
According to the ranking criteria of the Sentiment Vocabulary Ontology Library, the above buzzwords and emojis are artificially weighted and scored. Finally, the Sentiment Vocabulary Ontology Library, the bullet subtitle buzzword lexicon, and the emoji lexicon are fused together to get a relatively complete Chinese Bullet Subtitle Sentiment Lexicon. The weights and sentiment categories of some emojis are shown in Table 1.
In the problem of text sentiment analysis, sometimes it is necessary not only to know the sentiment tendency of the text but also to represent the sentiment quantitatively, while the current text affective computing method does not combine the characteristics of the bullet subtitle, resulting in poor quantitative accuracy of sentiment. In view of this, this paper proposes BS-CAL to describe the sentiment value of each dimension in detail. Some users deliberately choose some prominent bullet subtitle formats such as font size and color in order to express their strong sentiments when sending bullet subtitles. Therefore, BS-CAL combines the information of other dimensions of the bullet subtitle for quantitative calculation based on the traditional text affective computing method. The calculation formula of BS-CAL is as follows:where is the sentiment value of bullet subtitle under sentiment category , and is the gain intensity of the bullet subtitle itself. The calculation formula of is as follows:where is the set of sentiment words belonging to category , is the font size of the bullet subtitle, generally speaking, the bigger the font, the stronger the sentiment. is whether the color of the bullet subtitle is the default color black. The calculation formula of is as follows:
is the text sentiment value of bullet subtitle under sentiment category . The calculation formula is as follows:where is the number of negatives preceding the sentiment word , is the magnitude of the sentiment value of the word itself, is the set of sentiment punctuations immediately following the sentiment word , is the sentiment value of the sentiment punctuation, is the set of degree adverbs preceding the sentiment word , and is the strength of the degree adverbs. is the sentiment reversal variable of word in the sentiment category when calculating the sentiment category . The calculation formula of is as follows:
Assuming that the number of words of bullet subtitle is , the time complexity of BS-CAL is linear complexity . In the task of this paper, the bullet subtitle with sentiment words can be directly calculated according to the above formula, and the final sentiment classification can be obtained by judging the relationship between the sum of positive sentiment values and the sum of negative sentiment values.
In the problem of bullet subtitle text classification, there are many sentiment words that can play a decisive role in the result of text classification, but some bullet subtitle texts do not contain any sentiment words, which leads to the classification method based on the sentiment lexicon is not fully applicable in bullet subtitle sentiment analysis. In view of this, we divided the bullet subtitle into two categories for processing according to whether they contain sentiment words: when no sentiment words are included, the Gated Recurrent Unit classification model combined with the attention mechanism is used for prediction (ATT-GRU); when sentiment words are included, the heterogeneous ensemble learning method of BS-CAL, Naive Bayes, and ATT-GRU three models is adopted.
Using the three models as above can fuse the advantages of each model, ATT-GRU classification model based on Word2Vec makes full use of the semantic and positional relationships between words; the BS-CAL method is good at handling the bullet subtitle containing sentiment words and has a high performance in classifying the bullet subtitle with strong sentiment; Naive Bayes method based on sentiment lexicon fully considers the implicit influence brought by different combinations of sentiment words. The model construction is shown in Figure 3. 006ab0faaa