Intensity 1997 Subtitles
Francesc Benjamin
INTENSITY (1997)
"Chyna Shepherd is a twenty-six-year-old psychology student who survived an extremely troubled past. While visiting Laura Templeton's house for Thanksgiving, a farm in the Napa Valley, a serial killer named Edgler Foreman Vess breaks into the house, taking Laura and killing her parents. Chyna survives and manages to follow him."
MLA's rules appear to be that you use a colon for the first subtitle as well as the second. There are also some other rules regarding what to do if the title includes a question mark, ellipsis, or a colon already. MLA doesn't touch on more than 2 subtitles; however, there is a syntax for a work with more than one title which makes use of ; or, as the delimiter, so perhaps stay away from using semicolons.
Another alternative to having to manage all these subtitle files is to use a different container format. An AVI file is the most basic media container. It contains up to 1 video stream, and up to 1 audio stream. OGM supports up to 1 video stream, multiple audio streams, and multiple subtitles (I believe... I don't use OGM much anymore). MKV supports multiple video, multiple audio, and multiple subtitles.
But there are other differences not directly linked to the dialogue and language: most of the time, in any language, dubbing - while pretty accurate semantically - loses a ton in the "acting" department ; "dubbers" do say what they have to say, but comparing the original and the dubbed version, usually the latter lacks a lot of the original expressions, tone, intensity... basically the "emotional" message said is pretty different. (exception note: some older movies have great dubbing)
The +1 for dubbing is that you can keep your eyes on the movie... (I remember someone (American) talking about the movie "Amelie". He said "that's a nice movie, but I didn't know what is better, watch the movie (and miss dialogue) or read subtitles").
Tropical cyclones, a general term for hurricanes, typhoons, and tropical storms like Irene, don't just stick to the tropics. These storms can charge northward and wreak havoc in areas that normally wouldn't see this kind of extreme weather. Satellites provide us with near real-time information about the intensity of storms and where they're headed. Since its launch in 1997, TRMM, the Tropical Rainfall Measuring Mission, has remained a gold standard in collecting global rainfall data on storms.
Scott Braun: TRMM's usage for hurricanes has also been a major application. Operational agencies use it to get center fixes on storms to monitor how the internal structure of a storm is changing and how that might relate to the potential for a storm to either intensify or weaken.
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NHK launched a new AI English subtitling service in June 2022 using this Japanese-English AI translation system to add English subtitles to special news reports on General TV to be streamed live online. The AI English subtitling service is intended for non-Japanese residents and visitors to Japan. It will be used in the case of an earthquake of seismic intensity of lower 5 or above or when an emergency warning is issued such as a tsunami advisory, tsunami warning, major tsunami warning, or heavy rain warning. When the service is provided, it can be accessed via a banner on the NHK WORLD-JAPAN website and on the app.*1
Our emotions are complex psychological states and six emotions of happiness, sadness, anger, fear, surprise, and disgust are universally accepted15. Emotions and sentiments are related since sentiments are opinions of individuals, which are thought to be influenced by emotions. Sentiments can be positive, negative, or neutral and are expressed by individuals, mostly in the form of text. Sentiment analysis of various kind of texts such as tweets16, blogs17, and movie reviews18 is performed. Recently, studies have also started to work on sentiment analysis of movie subtitles19.
We hypothesize that the emotional aspect of movies change over time influencing the emotional and associated brain states of individuals dynamically. Furthermore, since a movie comprised of many components such as audio, video, and dialogues (subtitles), all of these should influence the emotional state of an individual in addition to his/her own pre-stimulus emotional state.
In order to perform sentiment classification of fMRI data collected during movie watching, we decided to use open-access data. However, none of these dataset provided time-based sentiment labels along with the fMRI data so we decided to generate these labels over time (dynamically) using the features of the movie itself. For this purpose, we used movie subtitles provided along with the movie. We classified sentiments using fMRI data with these labels to test our hypothesis. Our target was to perform classification with two (positive, negative) and three (positive, negative, neutral) sentiments using both lexicon-based (rule-based) and machine learning sentiment analyzers. Figure 2 shows the block diagram of our study. Description of various blocks is provided below.
We used three sentiment analyzers used from natural language processing, namely; (1) VADER (Valence Aware Dictionary for sEntiment Reasoning), (2) Textblob, and 3) Flair. VADER and Textblob are lexicon-based sentiment analyzers and compute polarity of a sentence based on the weights of its words from the lexicon55,56. However, they differ since in addition to lexicon, VADER also takes into account the emotional intensity of the text based on heuristics such as punctuation, emojis, and capitalization. The score of a text in VADER can be computed by adding up the intensity of each word within the text57.
All the three sentiment analyzers used in our study have their own special purposes; VADER works perfectly for social media contents, for example, Twitter based tweets. However, TextBlob works best for formal language, while Flair is trained on IMDB data and offers pre-trained models. None of them is specifically designed for sentiment analysis of movie subtitles and no subtitles specific sentiment analyzer is available. Consequently, we needed to do performance check of the three chosen sentiment analyzers. For this purpose, a simple algorithm was developed for similarity check of their results, which is described below.
After successful generation of sentiment based polarities from all the three sentiment analyzers, we classified the subtitles by assigning sentiment labels based on these polarities. The reason was to validate the use of polarities as labels for sentiment classification from subtitles before using them as labels for classification using fMRI data. For classification, the data was either divided in two classes (binary cases) or in three classes (3-Class) case. Sentiment labels (neutral = 0; negative = 1, positive = 2) were assigned to each sentence. Two basic classifiers were chosen, namely; (1) Random forest (RF) and (2) Support vector machine (SVM) for subtitles classification. RF is an ensemble learning method which works by producing number of decision trees during the training time and its accuracy does not get affected by over-fitting. SVM is a linear classifier that work on the basis of margin maximization and very effective in high dimensional spaces. These two classifiers were selected since they have been used in many studies for sentiment classification after labeling with VADER and/or Textblob63,64,65,66,67,68,69. The data was split into train-test split where training was done on 70%, testing on 20%, and validation on 10% of the subtitles.
In order to check if the results are indeed due to the correct labeling of the data from the subtitles, we performed the classification using shuffled labels, which are expected to mis-predict/mis-classify sentiments from both subtitles and fMRI. We performed randomization using a built in function of Python to our column of labels and applied them to classify the sentiments using both subtitles and fMRI. The results for subtitles classification with randomized labels are shown in Table 4.
The first novelty of our study is generation of labels by performing sentiment analysis of the movie subtitles for fMRI data classification. These labels were generated using three sentiment analyzers; (1) VADER (2) TextBlob and (3) Flair. VADER and Textblob are lexicon-based analyzers, while Flair is AI-based. These analyzers perform best under different scenarios for sentiment analysis from text data. For example, VADER performs best on social media data, which is from Facebook, Twitter etc and TextBlob works best with formal language. Flair is very simple to use, it has pre-trained sentiment analysis models and it is trained on IMDB data. Our data (subtitles) was different from the types of data on which these analyzers work best due to which we did not have any performance benchmark available. In the absence of any such benchmark, we did similarity check of their results. We found that the labels generated were reasonably similar with most of them above 65%. Comparison results showed best similarity between VADER and Textblob, followed by similarity between E-Flair and F-Flair. It shows that the labels generation is largely dependent upon type of the sentiment analyzers. Being lexicon-based VADER and Textblob produced similar results, and being ML-based E-Flair and F-Flair produced similar results. Furthermore, we observed overall better similarity scores for Binary Case 1. These results indicate the importance of choice of right sentiment analyzer based on the type of data to be analyzed.
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